**3. Results and Discussion**

## *3.1. D Pavement Distress Models*

## 3.1.1. Pavement Section 1

For each section under analysis, the results of the mobile imagery were compared to those from the camera. In Table 2**,** the specifications and results of the models for each device are shown for the survey of the first section. It can be observed that the GSD for the camera-based model was the lowest and this is expected given the higher resolution capable from this device. This is a direct result of the focal length of a professional camera being substantially higher than a mobile device. This also is the reason why the professional camera was used as a comparison model. However, the achieved GSD values for both devices were ~0.5 mm which is sufficient given the requirements for the detection of pavement distresses as previously examined in Section 2.1. The other parameter in Table 2 is the number of mesh faces of each model. This is a parameter that indicates the number of details on the models. However, it is hard to decipher based on the number and a visual inspection of the model is a better approach to analyze the models' details. Images of each replicated model for the section are given in Figures 7–9. Based on these figures, the details of the cracked section are shown. There were some differences in the colors of the models and this can be related to the internal parameters of the camera devices. The visual inspection, however, has no bearing on the accuracy for distress detection and a metric evaluation is needed to understand accuracies. and a metric evaluation is needed to understand accuracies. **Table 2.** Survey specifications for Distress 1. **Device Nikon D5200 Huawei P20 Pro Samsung Galaxy S9**  Distance from the pavement [mm] ~1500 ~1500 ~1500 Number of photos taken [-] 46 55 57 Ground sample distance (GSD) [mm/pixel] 0.241 0.547 0.505 Mesh faces created in SfM software [-] 4,800,185 2,155,780 2,900,791 and a metric evaluation is needed to understand accuracies. **Table 2.** Survey specifications for Distress 1. **Device Nikon D5200 Huawei P20 Pro Samsung Galaxy S9**  Distance from the pavement [mm] ~1500 ~1500 ~1500 Number of photos taken [-] 46 55 57 Ground sample distance (GSD) [mm/pixel] 0.241 0.547 0.505 Mesh faces created in SfM software [-] 4,800,185 2,155,780 2,900,791 **2020**, PEER REVIEW of visual inspection, no on detection needed to understand **2.** Survey for Distress 1. **Nikon Galaxy** Distance ~1500 Number of photos taken [-] 46 Ground sample [mm/pixel] 0.241 Mesh faces created in SfM software [-] 4,800,185 2,155,780 2,900,791

camera devices. The visual inspection, however, has no bearing on the accuracy for distress detection

camera devices. The visual inspection, however, has no bearing on the accuracy for distress detection

**Figure 7.** Model produced by imagery from camera. **Figure 7.** Model produced by imagery from camera. **Figure 7.** Model produced by imagery from camera.

**Figure 8.** Model produced by imagery from Huawei P20 Pro. **Figure 8.** Model produced by imagery from Huawei P20 Pro. **Figure 8.** Model produced by imagery from Huawei P20 Pro.

**Figure 9.** Model produced by imagery from Samsung Galaxy S9. **Figure 9.** Model produced by imagery from Samsung Galaxy S9. **Figure 9. Figure 9.**  Model produced by imagery from Samsung Galaxy S9. Model Galaxy S9.

**Table 2.** Survey specifications for Distress 1.


### 3.1.2. Pavement Section 2 3.1.2. Pavement Section 2

For the second pavement section, the resulting specifications from the models produced are given in Table 3. Similar to the first section, it is observed that the GSD was smaller for the camera derived model but the values for the models produced by the mobile phones were once again sufficient for detecting the pavement distresses. For the second pavement section, the resulting specifications from the models produced are given in Table 3. Similar to the first section, it is observed that the GSD was smaller for the camera derived model but the values for the models produced by the mobile phones were once again sufficient for detecting the pavement distresses.

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**Table 3.** Survey specifications for Distress 2. **Table 3.** Survey specifications for Distress 2.

The models produced by the mobile imagery for this section are shown in Figures 10 and 11. Also shown are images of the dense clouds produced by mobile imagery. These dense clouds allow for an inspection of the roughness and texture of the pavement and also allows the user to see clearly the distressed sections of the pavement. They are visualized here as this section also has a depressed area within the pavement and with the dense cloud, this is easier to visualize and detect. The models produced by the mobile imagery for this section are shown in Figures 10 and 11. Also shown are images of the dense clouds produced by mobile imagery. These dense clouds allow for an inspection of the roughness and texture of the pavement and also allows the user to see clearly the distressed sections of the pavement. They are visualized here as this section also has a depressed area within the pavement and with the dense cloud, this is easier to visualize and detect.

**Figure 10.** Model of distress 2—(**top**) and dense cloud—(**bottom**) produced by Huawei P20 Pro imagery. **Figure 10.** Model of distress 2—(**top**) and dense cloud—(**bottom**) produced by Huawei P20 Pro imagery.

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**Figure 11.** Model of distress 2—(**top**) and dense cloud—(**bottom**) produced by imagery from Samsung Galaxy s9. **Figure 11.** Model of distress 2—(**top**) and dense cloud—(**bottom**) produced by imagery from Samsung Galaxy s9.

### 3.1.3. Pavement Section 3 3.1.3. Pavement Section 3

severity analysis.

For the third section, the resulting specifications from the models produced are given in Table 4. Similar to the previous sections, the GSD was smaller for the camera derived model but the values for the models produced by the mobile phones were once again sufficient for detecting the pavement distresses. The models produced by the mobile imagery for this section are shown in Figures 12 and 13. Also displayed are images of the dense clouds produced by mobile imagery. Within the dense clouds, one can again observe the cracked section and the contours created by these cracks. This visualization can enable easier segmentation of the section for analysis of the cracks for metric For the third section, the resulting specifications from the models produced are given in Table 4. Similar to the previous sections, the GSD was smaller for the camera derived model but the values for the models produced by the mobile phones were once again sufficient for detecting the pavement distresses. The models produced by the mobile imagery for this section are shown in Figures 12 and 13. Also displayed are images of the dense clouds produced by mobile imagery. Within the dense clouds, one can again observe the cracked section and the contours created by these cracks. This visualization can enable easier segmentation of the section for analysis of the cracks for metric severity analysis.


**Table 4.** Survey specifications for distress 3.

Mesh faces created in SfM software [-] 3,151,044 1,486,123 1,900,926

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**Figure 12.** Model of distress 3 (**right**) and dense cloud (**left**) produced by imagery from Huawei P20 Pro. **Figure 12.** Model of distress 3 (**right**) and dense cloud (**left**) produced by imagery from Huawei P20 Pro. **Figure 12.** Model of distress 3 (**right**) and dense cloud (**left**) produced by imagery from Huawei P20 Pro.

**Figure 13.** Model (**right**) and dense cloud (**left**) produced by imagery from Samsung Galaxy s9. **Figure 13.** Model (**right**) and dense cloud (**left**) produced by imagery from Samsung Galaxy s9. **Figure 13.** Model (**right**) and dense cloud (**left**) produced by imagery from Samsung Galaxy s9.

### *3.2. Accuracy of 3D Models Generated by Imagery from Mobile Phones 3.2. Accuracy of 3D Models Generated by Imagery from Mobile Phones 3.2. Accuracy of 3D Models Generated by Imagery from Mobile Phones*

After the models were replicated using the SfM pipeline, they were then imported into CloudCompare, which is a software designed to analyze 3D models and point clouds. In the software, the models derived by the mobile imagery were aligned with the models derived by the camera. This alignment was done utilizing common points between the models so as to create a scenario where the two models are effectively overlapped at the correct points. Once the alignment was complete the distances between them were measured using a metric called C2C (Cloud to Cloud) absolute distance. This measurement produces a visualization of the measured differences across the model's surface. This visualization is also color coded with a color range of blue to red with blue highlighting smaller differences and red highlighting larger ones. From these differences, a histogram can be plotted illustrating the differences. Using the differences illustrated, the Weibull distribution was also applied to determine the Weibull parameters of shape and scale in order to have a statistical metric understanding of the differences. After the models were replicated using the SfM pipeline, they were then imported into CloudCompare, which is a software designed to analyze 3D models and point clouds. In the software, the models derived by the mobile imagery were aligned with the models derived by the camera. This alignment was done utilizing common points between the models so as to create a scenario where the two models are effectively overlapped at the correct points. Once the alignment was complete the distances between them were measured using a metric called C2C (Cloud to Cloud) absolute distance. This measurement produces a visualization of the measured differences across the model's surface. This visualization is also color coded with a color range of blue to red with blue highlighting smaller differences and red highlighting larger ones. From these differences, a histogram can be plotted illustrating the differences. Using the differences illustrated, the Weibull distribution was also applied to determine the Weibull parameters of shape and scale in order to have a statistical metric understanding of the differences. After the models were replicated using the SfM pipeline, they were then imported into CloudCompare, which is a software designed to analyze 3D models and point clouds. In the software, the models derived by the mobile imagery were aligned with the models derived by the camera. This alignment was done utilizing common points between the models so as to create a scenario where the two models are effectively overlapped at the correct points. Once the alignment was complete the distances between them were measured using a metric called C2C (Cloud to Cloud) absolute distance. This measurement produces a visualization of the measured differences across the model's surface. This visualization is also color coded with a color range of blue to red with blue highlighting smaller differences and red highlighting larger ones. From these differences, a histogram can be plotted illustrating the differences. Using the differences illustrated, the Weibull distribution was also applied to determine the Weibull parameters of shape and scale in order to have a statistical metric understanding of the differences.

### 3.2.1. Pavement Section 1 3.2.1. Pavement Section 1 3.2.1. Pavement Section 1

For the first section, the visualized differences along with the plotted distribution and Weibull plot are shown in Figures 14 and 15. The two important resulting parameters from this distribution are the Weibull shape and scale parameters and the values for these are given in Table 5 where the scale parameter would be measured in metres and the shape parameter has no dimension. The scale value typically specifies that 63.2 percentile of the distribution will fail before reaching this point [45]. Given the values in the table, this signifies that for all of the models this value was less than 0.003 m (3 mm). Furthermore, the value of the shape parameter was also close to 1 which signifies that within the distribution it is more likely that the majority of the values will occur early in the plot. Therefore, it can be inferred that for a random point on the model, it is likely that it would have a small measured For the first section, the visualized differences along with the plotted distribution and Weibull plot are shown in Figures 14 and 15. The two important resulting parameters from this distribution are the Weibull shape and scale parameters and the values for these are given in Table 5 where the scale parameter would be measured in metres and the shape parameter has no dimension. The scale value typically specifies that 63.2 percentile of the distribution will fail before reaching this point [45]. Given the values in the table, this signifies that for all of the models this value was less than 0.003 m (3 mm). Furthermore, the value of the shape parameter was also close to 1 which signifies that within the distribution it is more likely that the majority of the values will occur early in the plot. Therefore, it can be inferred that for a random point on the model, it is likely that it would have a small measured For the first section, the visualized differences along with the plotted distribution and Weibull plot are shown in Figures 14 and 15. The two important resulting parameters from this distribution are the Weibull shape and scale parameters and the values for these are given in Table 5 where the scale parameter would be measured in metres and the shape parameter has no dimension. The scale value typically specifies that 63.2 percentile of the distribution will fail before reaching this point [45]. Given the values in the table, this signifies that for all of the models this value was less than 0.003 m (3 mm). Furthermore, the value of the shape parameter was also close to 1 which signifies that within the distribution it is more likely that the majority of the values will occur early in the plot. Therefore, it can be inferred that for a random point on the model, it is likely that it would have a small measured

difference as the small differences are the values that occur early in the distribution plot. This helps to validate the hypothesis of using low-cost mobile imagery for this section. The visualizations provided also depicted the locations on the pavement where the most change is present. This was generally along the inside of the cracks as can be demonstrated in Figures 14 and 15. Future work will consider the range of values of the two Weibull parameters for a myriad of different distresses and phone types to try and establish more particular correlations and trends of these parameters based on the distresses. based on the distresses. **Table 5.** Weibull parameters observed from each model comparison for Distressed Section 1.  **Weibull Parameters**  Phone Shape (a) Scale (b) Huawei P20 Pro 1.186156 0.002275 Samsung Galaxy s9 0.981589 0.002794

and phone types to try and establish more particular correlations and trends of these parameters

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difference as the small differences are the values that occur early in the distribution plot. This helps to validate the hypothesis of using low-cost mobile imagery for this section. The visualizations provided also depicted the locations on the pavement where the most change is present. This was generally along the inside of the cracks as can be demonstrated in Figures 14 and 15. Future work

**Figure 14.** Measured differences between model generated by the camera and Huawei phone, (**top**) visualization of differences projected on model, (**bottom**)—distribution of measured differences. **Figure 14.** Measured differences between model generated by the camera and Huawei phone, (**top**)—visualization of differences projected on model, (**bottom**)—distribution of measured differences.

**Table 5.** Weibull parameters observed from each model comparison for Distressed Section 1.


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**Figure 15.** Measured differences between model generated by the camera and Samsung phone, (**top**)—visualization of differences projected on model, (**bottom**)—distribution of measured **Figure 15.** Measured differences between model generated by the camera and Samsung phone,(**top**)—visualization of differences projected on model, (**bottom**)—distribution of measured differences.

### differences. 3.2.2. Pavement Section 2

3.2.2. Pavement Section 2 For the second section, the visualized differences along with the plotted distribution and Weibull plot are shown in Figures 16 and 17. The Weibull shape and scale parameters are also given in Table 6. For this section, the values for the scale were again less than 0.003 m (3 mm). The value of the shape parameter was again close to 1 which once more signifies that within the distribution it is more likely that the majority of the values will occur early in the plot. This helps to reinforce the validity of the methodology for a different section, this one with depressions and cracking. The visualizations for these two comparisons showed that the most change occurred along the crack but also in the interior For the second section, the visualized differences along with the plotted distribution and Weibull plot are shown in Figures 16 and 17. The Weibull shape and scale parameters are also given in Table 6. For this section, the values for the scale were again less than 0.003 m (3 mm). The value of the shape parameter was again close to 1 which once more signifies that within the distribution it is more likely that the majority of the values will occur early in the plot. This helps to reinforce the validity of the methodology for a different section, this one with depressions and cracking. The visualizations for these two comparisons showed that the most change occurred along the crack but also in the interior of the depression present in the section.

of the depression present in the section. **Table 6.** Weibull parameters observed from each model comparison for distressed section 2.


Samsung Galaxy s9 1.005422 0.001528

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**Figure 16.** Measured differences between model generated by the camera and Huawei phone, (**right**) —visualization of differences projected on model, (**left**)—distribution of measured differences. **Figure 16.** Measured differences between model generated by the camera and Huawei phone, (**right**)—visualization of differences projected on model, (**left**)—distribution of measured differences. **Figure 16.** Measured differences between model generated by the camera and Huawei phone, (**right**) —visualization of differences projected on model, (**left**)—distribution of measured differences.

**Figure 17.** Measured differences between model generated by the camera and Samsung phone, (**right**)—visualization of differences projected on model, (**left**)—distribution of measured differences. **Figure 17.** Measured differences between model generated by the camera and Samsung phone, (**right**)—visualization of differences projected on model, (**left**)—distribution of measured differences. **Figure 17.** Measured differences between model generated by the camera and Samsung phone, (**right**)—visualization of differences projected on model, (**left**)—distribution of measured differences.

### 3.2.3. Pavement Section 3 3.2.3. Pavement Section 3 3.2.3. Pavement Section 3

For the third section, the visualized differences along with the plotted distribution and Weibull plot are shown in Figures 18 and 19. The Weibull shape and scale parameters are also given in Table 7. For this section the values for the scale were again less than 0.003 m (3 mm). Additionally as was the case with the two previous sections, the shape parameter was again close to 1 which once more signifies that within the distribution it is more likely that the majority of the values will occur early in the plot. Once more this reinforces the validity of the methodology for a different section, this one with area-wide cracking that are block and alligator-like. The visualizations for these two comparisons showed that the most change occurred along the interiors of the blocks of the crack For the third section, the visualized differences along with the plotted distribution and Weibull plot are shown in Figures 18 and 19. The Weibull shape and scale parameters are also given in Table 7. For this section the values for the scale were again less than 0.003 m (3 mm). Additionally as was the case with the two previous sections, the shape parameter was again close to 1 which once more signifies that within the distribution it is more likely that the majority of the values will occur early in the plot. Once more this reinforces the validity of the methodology for a different section, this one with area-wide cracking that are block and alligator-like. The visualizations for these two comparisons showed that the most change occurred along the interiors of the blocks of the crack For the third section, the visualized differences along with the plotted distribution and Weibull plot are shown in Figures 18 and 19. The Weibull shape and scale parameters are also given in Table 7. For this section the values for the scale were again less than 0.003 m (3 mm). Additionally as was the case with the two previous sections, the shape parameter was again close to 1 which once more signifies that within the distribution it is more likely that the majority of the values will occur early in the plot. Once more this reinforces the validity of the methodology for a different section, this one with area-wide cracking that are block and alligator-like. The visualizations for these two comparisons showed that the most change occurred along the interiors of the blocks of the crack

**Table 7.** Weibull parameters observed from each model comparison for distressed Section 3. **Table 7.** Weibull parameters observed from each model comparison for distressed Section 3. **Table 7.** Weibull parameters observed from each model comparison for distressed Section 3.


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**Figure 18.** Measured differences between model generated by the camera and Huawei phone, (**right**) —visualization of differences projected on model, (**left**)—distribution of measured differences. **Figure 18.** Measured differences between model generated by the camera and Huawei phone, (**right**)—visualization of differences projected on model, (**left**)—distribution of measured differences. **Figure 18.** Measured differences between model generated by the camera and Huawei phone, (**right**) —visualization of differences projected on model, (**left**)—distribution of measured differences.

**Figure 19.** Measured differences between model generated by the camera and Samsung phone, (**right**)—visualization of differences projected on model, (**left**)—distribution of measured differences. **Figure 19.** Measured differences between model generated by the camera and Samsung phone, (**right**)—visualization of differences projected on model, (**left**)—distribution of measured differences. **Figure 19.** Measured differences between model generated by the camera and Samsung phone, (**right**)—visualization of differences projected on model, (**left**)—distribution of measured differences.

### *3.3. Application of RANSAC Segmentation 3.3. Application of RANSAC Segmentation 3.3. Application of RANSAC Segmentation*

Once the accuracy of the models was demonstrated as shown in the previous section, the next step was the application of the segmentation strategies to try and isolate the distresses occurring on each pavement section in the case study. The first considered strategy was the use of the RANSAC algorithm to extract shapes from the point clouds. Once the accuracy of the models was demonstrated as shown in the previous section, the next step was the application of the segmentation strategies to try and isolate the distresses occurring on each pavement section in the case study. The first considered strategy was the use of the RANSAC algorithm to extract shapes from the point clouds. Once the accuracy of the models was demonstrated as shown in the previous section, the next step was the application of the segmentation strategies to try and isolate the distresses occurring on each pavement section in the case study. The first considered strategy was the use of the RANSAC algorithm to extract shapes from the point clouds.

The first step for this implementation was assigning a value of the minimum support points per primitive. For each of the models being analyzed the total number of points was between 1.4 to 4.5 million points. Additionally, each model assumed a physical distance of about 2 to 4 m2 on ground. Given these factors, a value of 50,000 was assigned as this would split the object into no more than 90 segments and given the fact that only one plane was required as a reference case this number would limit the algorithm from producing planes cutting through the model at different mismatched angles. To ensure this value was correct the algorithm was applied for smaller values of 500, 5000, 10,000 and each of these scenarios inappropriate planes were generated as shown in Figure 20. This process was tried for each model and it was shown that with 50,000 points the result would yield an appropriate reference plane as shown through an example of one of the applications in Figure 21. On this, the plane appropriately cuts through the model to create a valid reference plane. The first step for this implementation was assigning a value of the minimum support points per primitive. For each of the models being analyzed the total number of points was between 1.4 to 4.5 million points. Additionally, each model assumed a physical distance of about 2 to 4 m2 on ground. Given these factors, a value of 50,000 was assigned as this would split the object into no more than 90 segments and given the fact that only one plane was required as a reference case this number would limit the algorithm from producing planes cutting through the model at different mismatched angles. To ensure this value was correct the algorithm was applied for smaller values of 500, 5000, 10,000 and each of these scenarios inappropriate planes were generated as shown in Figure 20. This process was tried for each model and it was shown that with 50,000 points the result would yield an appropriate reference plane as shown through an example of one of the applications in Figure 21. On this, the plane appropriately cuts through the model to create a valid reference plane. The first step for this implementation was assigning a value of the minimum support points per primitive. For each of the models being analyzed the total number of points was between 1.4 to 4.5 million points. Additionally, each model assumed a physical distance of about 2 to 4 m<sup>2</sup> on ground. Given these factors, a value of 50,000 was assigned as this would split the object into no more than 90 segments and given the fact that only one plane was required as a reference case this number would limit the algorithm from producing planes cutting through the model at different mismatched angles. To ensure this value was correct the algorithm was applied for smaller values of 500, 5000, 10,000 and each of these scenarios inappropriate planes were generated as shown in Figure 20. This process was tried for each model and it was shown that with 50,000 points the result would yield an appropriate reference plane as shown through an example of one of the applications in Figure 21. On this, the plane appropriately cuts through the model to create a valid reference plane.

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**Figure 20.** Application of random sampling consensus (RANSAC) algorithm with too small of a value for the number of minimum support points. **Figure 20.** Application of random sampling consensus (RANSAC) algorithm with too small of a value for the number of minimum support points. **Figure 20.** Application of random sampling consensus (RANSAC) algorithm with too small of a value for the number of minimum support points.

**Figure 20.** Application of random sampling consensus (RANSAC) algorithm with too small of a value

**Figure 21.** Application of plane shape through RANSAC algorithm. **Figure 21.** Application of plane shape through RANSAC algorithm. **Figure 21.** Application of plane shape through RANSAC algorithm.

Once this plane was adequately assigned a distance computation between the plane mesh and the point cloud was done utilizing the C2M distance computation in CloudCompare to produce a depth map for each distress. The C2M distances represent the depths and filtering this can result in the segmentation of the model. This is illustrated in Figures 22–24. Once this plane was adequately assigned a distance computation between the plane mesh and the point cloud was done utilizing the C2M distance computation in CloudCompare to produce a depth map for each distress. The C2M distances represent the depths and filtering this can result in the segmentation of the model. This is illustrated in Figures 22–24. Once this plane was adequately assigned a distance computation between the plane mesh and the point cloud was done utilizing the C2M distance computation in CloudCompare to produce adepth map for each distress. The C2M distances represent the depths and filtering this can result in the segmentation of the model. This is illustrated in Figures 22–24. **Figure 21.** Application of plane shape through RANSAC algorithm. Once this plane was adequately assigned a distance computation between the plane mesh and the point cloud was done utilizing the C2M distance computation in CloudCompare to produce a depth map for each distress. The C2M distances represent the depths and filtering this can result in

the segmentation of the model. This is illustrated in Figures 22–24.

**Figure 22.** Pavement section 1 with the depth map created. **Figure 22.** Pavement section 1 with the depth map created. **Figure 22.** Pavement section 1 with the depth map created. **Figure 22.** Pavement section 1 with the depth map created.

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**Figure 23.** Pavement section 2 with the depth map created. **Figure 23.** Pavement section 2 with the depth map created. **Figure 23.** Pavement section 2 with the depth map created.

**Figure 24.** Pavement section 3 with the depth map created. **Figure 24.** Pavement section 3 with the depth map created. **Figure 24.** Pavement section 3 with the depth map created.

These depth maps now allow filtering to be done by depth and section and allow the model to be segmented for the sections to be analyzed. This is easily done by controlling the range of the depth map and this is illustrated in Figures 25–27. From the current segmentation result, the process can detect typical distresses where there is a change in surface deviation of the pavement. As the major groups of pavements distresses are cracking distresses and visco-plastic deformations (which both feature this type of deviation), this process and segmentation can account for most distresses. Viscoplastic deformations include bumps, sagging, rutting, corrugations, depressions, potholes, swelling, lane and shoulder drop off, shoving, and stripping. In Figures 25–27, the depth maps for the These depth maps now allow filtering to be done by depth and section and allow the model to be segmented for the sections to be analyzed. This is easily done by controlling the range of the depth map and this is illustrated in Figures 25–27. From the current segmentation result, the process can detect typical distresses where there is a change in surface deviation of the pavement. As the major groups of pavements distresses are cracking distresses and visco-plastic deformations (which both feature this type of deviation), this process and segmentation can account for most distresses. Viscoplastic deformations include bumps, sagging, rutting, corrugations, depressions, potholes, swelling, lane and shoulder drop off, shoving, and stripping. In Figures 25–27, the depth maps for the These depth maps now allow filtering to be done by depth and section and allow the model to be segmented for the sections to be analyzed. This is easily done by controlling the range of the depth map and this is illustrated in Figures 25–27. From the current segmentation result, the process can detect typical distresses where there is a change in surface deviation of the pavement. As the major groups of pavements distresses are cracking distresses and visco-plastic deformations (which both feature this type of deviation), this process and segmentation can account for most distresses. Visco-plastic deformations include bumps, sagging, rutting, corrugations, depressions, potholes, swelling, lane and shoulder drop off, shoving, and stripping. In Figures 25–27, the depth maps for the pavement sections

are shown at three different levels of segmentation. In the first image, the entire section is visualized with the hotspots of the distressed sections highlighted. Given that in this depth map one can see the particular points of interest, the depth map was then further segmented to remove the parts of the pavement without distress by using a smaller range on the depth map values. The final image of the three further adjusts the range of the depth map to then only illustrate the distressed section with the deepest distress. In Figure 25, the third image highlights the section suffering from not only cracking but also a significant depression. The same process was carried out for each section and the visualizations are shown in Figures 26 and 27 also follow the same methodology to allow visualization of the exact points of distress and to isolate these sections. In Figure 26, the exact section suffering from a pothole is isolated and in Figure 27, the exact section suffering from excessive cracking is isolated. The exact metric evaluation of the distress is not shown within the study as the metric evaluation of 3D models derived from SfM processes has been previously validated using laser equipment to verify the metric accuracy of the models to determine the closeness of the results from those measured within the field [20,27]. The semantic color choice of the depth map is up to the user for the visualizations, in terms of which colors signify positive or negative deviations. Once this segmentation is done, metric assessments of the segmented portion can be found such as the area and volume of the segmented region which can either be the section that is distressed or the section that is not. By doing this, a ratio of the distressed section to non-distressed section can be established and inserted into the asset database for the road authorities, which is critical for establishing appropriate pavement management strategies. These critiques are possible as all of the models are scaled and the previous sections have established the metric accuracy of these scaled models. The dimensional analysis of the sections was not carried out as the determination of the methodology to arrive at a position at which this type of analysis is possible was more important to the discussion of the study and the research. Additionally features such as the depressed section and the crack section be filtered by simply changing the range of the depth map as shown in the images. From this segmentation, a differentiation of the types of distresses occurring can be made as well as the particular features of the distress can be more easily identified as the depth maps shown in Figures 25–27 establish isolation of sections that have related features. At this point, the user would be able to identify the particular distress type. This will provide a road agency with exact measurements of the distress which can be utilized for severity assessment and to trigger times for maintenance and rehabilitation interventions. *Infrastructures* **2020**, *5*, x FOR PEER REVIEW 19 of 25 pavement sections are shown at three different levels of segmentation. In the first image, the entire section is visualized with the hotspots of the distressed sections highlighted. Given that in this depth map one can see the particular points of interest, the depth map was then further segmented to remove the parts of the pavement without distress by using a smaller range on the depth map values. The final image of the three further adjusts the range of the depth map to then only illustrate the distressed section with the deepest distress. In Figure 25, the third image highlights the section suffering from not only cracking but also a significant depression. The same process was carried out for each section and the visualizations are shown in Figures 26 and 27 also follow the same methodology to allow visualization of the exact points of distress and to isolate these sections. In Figure 26, the exact section suffering from a pothole is isolated and in Figure 27, the exact section suffering from excessive cracking is isolated. The exact metric evaluation of the distress is not shown within the study as the metric evaluation of 3D models derived from SfM processes has been previously validated using laser equipment to verify the metric accuracy of the models to determine the closeness of the results from those measured within the field [20,27]. The semantic color choice of the depth map is up to the user for the visualizations, in terms of which colors signify positive or negative deviations. Once this segmentation is done, metric assessments of the segmented portion can be found such as the area and volume of the segmented region which can either be the section that is distressed or the section that is not. By doing this, a ratio of the distressed section to nondistressed section can be established and inserted into the asset database for the road authorities, which is critical for establishing appropriate pavement management strategies. These critiques are possible as all of the models are scaled and the previous sections have established the metric accuracy of these scaled models. The dimensional analysis of the sections was not carried out as the determination of the methodology to arrive at a position at which this type of analysis is possible was more important to the discussion of the study and the research. Additionally features such as the depressed section and the crack section be filtered by simply changing the range of the depth map as shown in the images. From this segmentation, a differentiation of the types of distresses occurring can be made as well as the particular features of the distress can be more easily identified as the depth maps shown in Figures 25–27 establish isolation of sections that have related features. At this point, the user would be able to identify the particular distress type. This will provide a road agency with exact measurements of the distress which can be utilized for severity assessment and to trigger times for maintenance and rehabilitation interventions.

**Figure 25.** Segmentation of pavement section using RANSAC. **Figure 25.** Segmentation of pavement section using RANSAC.

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**Figure 26.** Segmentation of pavement section 2 using RANSAC. **Figure 26.** Segmentation of pavement section 2 using RANSAC. **Figure 26.** Segmentation of pavement section 2 using RANSAC.

**Figure 27.** Segmentation of pavement section 3 with RANSAC. **Figure 27.** Segmentation of pavement section 3 with RANSAC.

### *3.4. Application of Fit Segmentation 3.4. Application of Fit Segmentation 3.4. Application of Fit Segmentation* **Figure 27.** Segmentation of pavement section 3 with RANSAC.

demonstrated in Figure 28.

For the application of the fit algorithm, a similar process was followed to that of the RANSAC wherein a plane was generated considering the collection of points within the point cloud. This application can be considered as a simpler method given the fact that it relies on a standard least square fitting methodology. The application of the fit tool was carried out on each model and this is demonstrated in Figure 28. For the application of the fit algorithm, a similar process was followed to that of the RANSAC wherein a plane was generated considering the collection of points within the point cloud. This application can be considered as a simpler method given the fact that it relies on a standard least square fitting methodology. The application of the fit tool was carried out on each model and this is demonstrated in Figure 28. For the application of the fit algorithm, a similar process was followed to that of the RANSAC wherein a plane was generated considering the collection of points within the point cloud. This application can be considered as a simpler method given the fact that it relies on a standard least square fitting methodology. The application of the fit tool was carried out on each model and this is demonstrated in Figure 28. *3.4. Application of Fit Segmentation*  For the application of the fit algorithm, a similar process was followed to that of the RANSAC wherein a plane was generated considering the collection of points within the point cloud. This application can be considered as a simpler method given the fact that it relies on a standard least square fitting methodology. The application of the fit tool was carried out on each model and this is

**Figure 28.** Application of 'fit' plane to each pavement section. **Figure 28. Figure 28.** Application of 'fit' plane to each pavement section. Application of 'fit' plane to each pavement section.

or agency.

Following this plane application, the depth maps were generated on each model similar to the application of the RANSAC. This was done for the same purpose as previously stated to allow for segmenting particular sections of the pavement sections. This is illustrated in Figures 29–31. particular points of interest by altering the range of the displayed depth map. The segmented images depict particular points of interest. Each isolation can be measured and the metric value recorded for the purpose of collecting asset information and storing in within the database of the road authority or agency. represent three levels of segmentation for each distress and highlight the possibility of isolating particular points of interest by altering the range of the displayed depth map. The segmented images depict particular points of interest. Each isolation can be measured and the metric value recorded for the purpose of collecting asset information and storing in within the database of the road authority

represent three levels of segmentation for each distress and highlight the possibility of isolating

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segmenting particular sections of the pavement sections. This is illustrated in Figures 29–31.

Following this plane application, the depth maps were generated on each model similar to the application of the RANSAC. This was done for the same purpose as previously stated to allow for

Following this plane application, the depth maps were generated on each model similar to the application of the RANSAC. This was done for the same purpose as previously stated to allow for

Similar to the segmented images of the sections by the RANSAC algorithm, these images

**Figure 29.** Segmented pavement section 1 using fit plane. **Figure 29.** Segmented pavement section 1 using fit plane. **Figure 29.** Segmented pavement section 1 using fit plane.

**Figure 30.** Segmented pavement section 2 using fit plane. **Figure 30. Figure 30.**  Segmented pavement section 2 using fit plane. Segmented pavement section 2 using fit plane.

Similar to the segmented images of the sections by the RANSAC algorithm, these images represent three levels of segmentation for each distress and highlight the possibility of isolating particular points of interest by altering the range of the displayed depth map. The segmented images depict particular points of interest. Each isolation can be measured and the metric value recorded for the purpose of collecting asset information and storing in within the database of the road authority or agency.

Figure 29 demonstrate the capacity of using the fit plane. In each segmentation particular sections can be isolated. The sections where there is cracking can be isolated or the sections where there is a depression. The same can be done for the sections which are in suitable conditions. The results are similar to those obtained with the RANSAC. A metric difference between the segmented models was not however done as more examples are needed for this to be done. This will be explored in future works.

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**Figure 31.** Segmented pavement section 3 using fit plane. **Figure 31.** Segmented pavement section 3 using fit plane.

Figure 29 demonstrate the capacity of using the fit plane. In each segmentation particular sections can be isolated. The sections where there is cracking can be isolated or the sections where there is a depression. The same can be done for the sections which are in suitable conditions. The results are similar to those obtained with the RANSAC. A metric difference between the segmented models was not however done as more examples are needed for this to be done. This will be explored in future works. The essential conclusion from both segmentation applications is that the process has the capacity to produce the resulting isolated sections of the mobile imagery-based 3D models. These isolated segments can yield critical metric information as they are scaled and the accuracy of the derived models was shown to be sufficient for the purpose of detecting pavement distresses. The extraction of the metric information of the distresses and the segmented sections are not carried out within this The essential conclusion from both segmentation applications is that the process has the capacity to produce the resulting isolated sections of the mobile imagery-based 3D models. These isolated segments can yield critical metric information as they are scaled and the accuracy of the derived models was shown to be sufficient for the purpose of detecting pavement distresses. The extraction of the metric information of the distresses and the segmented sections are not carried out within this study but the previous analysis of 3D models originating from an SfM pipeline have already demonstrated this possibility [20]. The methodology of segmenting the imagery and thus exploiting it for further analysis is the important outcome of this aspect of the study. Additionally, this process was done with user-friendly algorithms that can be practically repeated and do not require substantial processing power or exhaustive timelines utilized by other pipelines.

### study but the previous analysis of 3D models originating from an SfM pipeline have already **4. Conclusions**

demonstrated this possibility [20]. The methodology of segmenting the imagery and thus exploiting it for further analysis is the important outcome of this aspect of the study. Additionally, this process was done with user-friendly algorithms that can be practically repeated and do not require substantial processing power or exhaustive timelines utilized by other pipelines. **4. Conclusions**  This work provided within this paper had two particular purposes: demonstrate the accuracies of utilizing imagery from mobile phones for creating 3D models of pavement distresses and secondly to consider practical and efficient means of segmenting these models to isolate pavement distresses. This work provided within this paper had two particular purposes: demonstrate the accuracies of utilizing imagery from mobile phones for creating 3D models of pavement distresses and secondly to consider practical and efficient means of segmenting these models to isolate pavement distresses. The purpose, therefore, was to establish a workable pipeline with mobile phone devices. To carry out these tasks, surveys were carried out on distressed pavement sections within the city of Palermo, Italy where there are a substantial number of distressed roads. Three sections were considered which had commonly found pavement distresses. Each section was surveyed with two common mobile phones and a professional camera was used as a control in the experiment.

The purpose, therefore, was to establish a workable pipeline with mobile phone devices. To carry out these tasks, surveys were carried out on distressed pavement sections within the city of Palermo, Italy where there are a substantial number of distressed roads. Three sections were considered which had commonly found pavement distresses. Each section was surveyed with two common mobile phones and a professional camera was used as a control in the experiment. Using statistical analysis and comparison, it was found that the SfM techniques discussed can be utilized with the mobile devices used in the study with accurate models being generated that can sufficiently detect the precedence of pavement distresses within the sections. The statistical analysis was done utilizing the Weibull distribution evaluation with comparisons being made from models generated from the mobile devices to the models generated from a professional camera. The Weibull parameters yielded in the evaluation detailed that the majority of the deviations between the models are of very small values, in the range of less than 3 mm. This value allows for authentication of the pipeline with the mobile devices based on the typical measurement of common pavement distresses. This represents a novel approach to the problem and based on advances in the phone industry future results will be even more promising. Furthermore, it should be noted that the mobile phones used in the study are not the latest flagship models from their respective companies and that there are newer models currently on the market which have better cameras and therefore, would likely yield models Using statistical analysis and comparison, it was found that the SfM techniques discussed can be utilized with the mobile devices used in the study with accurate models being generated that can sufficiently detect the precedence of pavement distresses within the sections. The statistical analysis was done utilizing the Weibull distribution evaluation with comparisons being made from models generated from the mobile devices to the models generated from a professional camera. The Weibull parameters yielded in the evaluation detailed that the majority of the deviations between the models are of very small values, in the range of less than 3 mm. This value allows for authentication of the pipeline with the mobile devices based on the typical measurement of common pavement distresses. This represents a novel approach to the problem and based on advances in the phone industry future results will be even more promising. Furthermore, it should be noted that the mobile phones used in the study are not the latest flagship models from their respective companies and that there are newer models currently on the market which have better cameras and therefore, would likely yield models with greater resolutions. This demonstrates the capacity and sustainability of the pipeline moving forward. Further work can include further assessments of other mobile phones and more types of pavement distresses. Additionally, it was must be mentioned that the use of mobile devices poses advantages to other methods of obtaining imagery such as drones which have legal restrictions in many countries. For future work imagery from Google Earth's platform can also be combined with

imagery from the mobile phones to have a bigger model developed encompassing a full network and not just a particular section. This is possible as the imagery from Google Earth has been integrated with on-ground 2D imagery for applications in other fields [46].

After the considerations of the accuracy of the 3D models, the study carried out a brief analysis of the segmentation of the 3D imagery. Planes were constructed on the models utilizing different algorithms for the purpose of creating depth maps and these depth maps were then filtered based on the location and presence of distresses within the pavement section. By filtering the models, the model was segmented based on similarly observed features which can allow for a differentiation of the different pavement distress categories. Additionally, the segmentation of the particular points of interest allows the user to obtain a ratio of the distressed area to the non-distressed area on the pavement section which is a valuable attribute for the road authority's database. This demonstrated the capacity of the segmentation pipeline to pinpoint occurring distresses and obtain metric information on them. Further work needs to be done on larger sections and more types of pavement distresses to establish a clear segmentation pipeline for these types of images. Nevertheless, the segmentation strategies used were demonstrated to be potent enough to isolate the pavement distresses and this establishes a path forward towards the full low-cost automation of road condition data acquisition and analysis for a pavement management.

**Author Contributions:** Conceptualization, R.R.; methodology, R.R., L.I., and G.D.M.; software, R.R. and L.I.; validation, R.R. and G.D.M.; formal analysis, R.R. and G.D.M.; investigation, R.R. and G.D.M.; resources, G.D.M. and L.I.; data curation, R.R.; writing—original draft preparation, R.R.; writing—review and editing, R.R., G.D.M., and L.I.; visualization, R.R. and G.D.M. All authors have read and agreed to the published version of the manuscript.

**Funding:** The research presented in this paper was carried out as part of the SMARTI ETN project under the H2020-MSCA-ETN-2016. This project has received funding from the European Union's H2020 Programme for research, technological development and demonstration under grant agreement number 721493.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.
