Author Contributions
Conceptualization, L.Y., H.L. and Y.L.; Methodology, L.Y. and P.Y.; Software, L.Y., Q.S. and H.L.; Validation, P.Y.; Formal Analysis, X.Z.; Investigation, L.Y.; Resources, L.Y.; Data Curation, X.Z.; Writing-original draft preparation, L.Y., X.Z., Q.S., P.Y. and Y.L.; Writing-review and editing, L.Y., P.Y., Q.S., and Y.L.; Supervision, L.Y., Y.L. and P.Y.; Funding acquisition, L.Y. and X.Z. All authors have read and agreed to the published version of the manuscript.
Figure 1.
Schematic diagram of heat conduction theory: (a) Solid model; (b) Diagram of infinitesimal analysis; (c) A slab of specimen is excited by a pulsed heat source.
Figure 1.
Schematic diagram of heat conduction theory: (a) Solid model; (b) Diagram of infinitesimal analysis; (c) A slab of specimen is excited by a pulsed heat source.
Figure 2.
Schematic diagram to illustrate the sensitive area (C) of defects (A) in the specimen (I).
Figure 2.
Schematic diagram to illustrate the sensitive area (C) of defects (A) in the specimen (I).
Figure 3.
The history of standard deviation of the sensitive area with time.
Figure 3.
The history of standard deviation of the sensitive area with time.
Figure 4.
Block diagram for extraction of defects automatically and accurately.
Figure 4.
Block diagram for extraction of defects automatically and accurately.
Figure 5.
Photos of a polyvinyl chloride (PVC) test specimen: (a) The surface backs to IR camera; (b) The surface facing the IR camera.
Figure 5.
Photos of a polyvinyl chloride (PVC) test specimen: (a) The surface backs to IR camera; (b) The surface facing the IR camera.
Figure 6.
Schematics of PVC test specimen: (a) Main view: Distribution of defects; (b) Right View: Depth of defect #1; (c) Left View: Depth of defect #2.
Figure 6.
Schematics of PVC test specimen: (a) Main view: Distribution of defects; (b) Right View: Depth of defect #1; (c) Left View: Depth of defect #2.
Figure 7.
Synchronized infrared image acquisition system.
Figure 7.
Synchronized infrared image acquisition system.
Figure 8.
Distribution of temperature field on the surface of the PVC test object in the sequence.
Figure 8.
Distribution of temperature field on the surface of the PVC test object in the sequence.
Figure 9.
The surface temperature of the shallower defect with frame.
Figure 9.
The surface temperature of the shallower defect with frame.
Figure 10.
Image composition.
Figure 10.
Image composition.
Figure 11.
Image processing of image composition: (a) Median filter; (b) Segmentation by Otsu’s method.
Figure 11.
Image processing of image composition: (a) Median filter; (b) Segmentation by Otsu’s method.
Figure 12.
Blob analysis and edge extraction in each blob: (a) Blob analysis; (b) Edge abstracted by Canny operator.
Figure 12.
Blob analysis and edge extraction in each blob: (a) Blob analysis; (b) Edge abstracted by Canny operator.
Figure 13.
Single sensitive region on each defect is randomly selected.
Figure 13.
Single sensitive region on each defect is randomly selected.
Figure 14.
A suitable frame for each defect is determined by the maximum of sandard deviation of sensitive region method (MSDSRM) using single sensitive region.
Figure 14.
A suitable frame for each defect is determined by the maximum of sandard deviation of sensitive region method (MSDSRM) using single sensitive region.
Figure 15.
A sketch of the region of interest (ROI) based on the blob and restrained by constraints.
Figure 15.
A sketch of the region of interest (ROI) based on the blob and restrained by constraints.
Figure 16.
Defect extraction of primary detection: (a) ROI of defect #1 from Frame 178; (b) Segmentation of defect #1; (c) ROI of defect #1 from Frame 199; (d) Segmentation of defect #2; (e) Composition segmentation images as a whole.
Figure 16.
Defect extraction of primary detection: (a) ROI of defect #1 from Frame 178; (b) Segmentation of defect #1; (c) ROI of defect #1 from Frame 199; (d) Segmentation of defect #2; (e) Composition segmentation images as a whole.
Figure 17.
Discrete distribution of relative deviation of defect area for 100 times primary detection.
Figure 17.
Discrete distribution of relative deviation of defect area for 100 times primary detection.
Figure 18.
Two sensitive regions selection methods: (a) Multiple sensitive areas selected uniformly; (b) 50 multiple sensitive areas selected randomly.
Figure 18.
Two sensitive regions selection methods: (a) Multiple sensitive areas selected uniformly; (b) 50 multiple sensitive areas selected randomly.
Figure 19.
The MSDSRM to select a suitable frame for each defect: (a) Curves of standard deviation vs. time based on uniform selection of sensitive regions; (b) Curves of standard deviation vs. time based on random selection of sensitive regions.
Figure 19.
The MSDSRM to select a suitable frame for each defect: (a) Curves of standard deviation vs. time based on uniform selection of sensitive regions; (b) Curves of standard deviation vs. time based on random selection of sensitive regions.
Figure 20.
The scatter diagram of the experiment conducted 100 times: (a) The scatter diagram of the experiment conducted 100 times by uniform selection; (b) The scatter diagram of the experiment conducted 100 times by random selection.
Figure 20.
The scatter diagram of the experiment conducted 100 times: (a) The scatter diagram of the experiment conducted 100 times by uniform selection; (b) The scatter diagram of the experiment conducted 100 times by random selection.
Figure 21.
Discrete distribution of relative deviation of defect area: (a) Discrete distribution of relative deviation of defect area using uniform selection; (b) Discrete distribution of relative deviation of defect area using random selection.
Figure 21.
Discrete distribution of relative deviation of defect area: (a) Discrete distribution of relative deviation of defect area using uniform selection; (b) Discrete distribution of relative deviation of defect area using random selection.
Figure 22.
The maximum standard deviation surface related to width and height: (a) The maximum standard deviation surface for #1; (b) The maximum standard deviation surface for #2.
Figure 22.
The maximum standard deviation surface related to width and height: (a) The maximum standard deviation surface for #1; (b) The maximum standard deviation surface for #2.
Figure 23.
The 3D scatter diagram of selection frames with the sensitive region size variation: (a) The 3D scatter diagram of selection frames for #1; (b) The 3D scatter diagram of selection frames for #2.
Figure 23.
The 3D scatter diagram of selection frames with the sensitive region size variation: (a) The 3D scatter diagram of selection frames for #1; (b) The 3D scatter diagram of selection frames for #2.
Figure 24.
The scatter diagram of the experiment conducted 100 times to be compared with
Figure 19: (
a) The scatter diagram of the experiment conducted 100 times by uniform selection; (
b) The scatter diagram of the experiment conducted 100 times by random selection.
Figure 24.
The scatter diagram of the experiment conducted 100 times to be compared with
Figure 19: (
a) The scatter diagram of the experiment conducted 100 times by uniform selection; (
b) The scatter diagram of the experiment conducted 100 times by random selection.
Figure 25.
Schematic diagram of defect area and sound area sampling.
Figure 25.
Schematic diagram of defect area and sound area sampling.
Figure 26.
The curves of average temperature difference between the defect area and sound area over time.
Figure 26.
The curves of average temperature difference between the defect area and sound area over time.
Figure 27.
The curve of average temperature elevation over time and the acquired frame image.
Figure 27.
The curve of average temperature elevation over time and the acquired frame image.
Figure 28.
The specimen of impact damage of carbon fiber-reinforced plastics (CFRP).
Figure 28.
The specimen of impact damage of carbon fiber-reinforced plastics (CFRP).
Figure 29.
Delamination defect detected by UT and its extraction: (a) Delamination defect detected by UT; (b) ROI; (c) Defect segmentation.
Figure 29.
Delamination defect detected by UT and its extraction: (a) Delamination defect detected by UT; (b) ROI; (c) Defect segmentation.
Figure 30.
Delamination defect detected by long-pulse thermography.
Figure 30.
Delamination defect detected by long-pulse thermography.
Figure 31.
Delamination defect extracted by MSDSRM: (a) ROI of Frame 150; (b) Binary image; (c) Sensitive areas selected; (d) Curves of standard deviation vs. time based on random selection of sensitive regions; (e) ROI of Frame 164; (f) Defect segmentation.
Figure 31.
Delamination defect extracted by MSDSRM: (a) ROI of Frame 150; (b) Binary image; (c) Sensitive areas selected; (d) Curves of standard deviation vs. time based on random selection of sensitive regions; (e) ROI of Frame 164; (f) Defect segmentation.
Figure 32.
Delamination defect extracted by MTDM: (a) Schematic diagram of defect area and sound area sampling; (b) The curves of average temperature difference between the defect area and sound area over time; (c) ROI in Frame 304; (d) Defect extraction.
Figure 32.
Delamination defect extracted by MTDM: (a) Schematic diagram of defect area and sound area sampling; (b) The curves of average temperature difference between the defect area and sound area over time; (c) ROI in Frame 304; (d) Defect extraction.
Table 1.
Statistics of defect parameters on image composition processing.
Table 1.
Statistics of defect parameters on image composition processing.
Defect Number | Object | Threshold | Defect Number | Defect Pixel | Relative Error |
---|
#1 | Image composition | 0.47 | #1 | 4286.5 | 27.08% |
#2 | #2 | 2188.3 | 34.47% |
Table 2.
Statistics of defect parameters using a single sensitive region.
Table 2.
Statistics of defect parameters using a single sensitive region.
Defect Number | Frame | Threshold | Defect Pixel | Relative Error |
---|
#1 | 178 | 0.46 | 3443.1 | 2.08% |
#2 | 199 | 0.36 | 3242.1 | 2.91% |
Table 3.
Statistics of defect parameters using multiple sensitive regions.
Table 3.
Statistics of defect parameters using multiple sensitive regions.
Defect Number | Uniform Selection | Random Selection |
---|
Frame | Defect Pixel | Relative Error | Frame | Defect Pixel | Relative Error |
---|
#1 | 164 | 3404.7 | 0.94% | 166 | 3384.2 | 0.33% |
#2 | 183 | 3420.8 | 2.44% | 186 | 3430.5 | 2.73% |
Table 4.
Statistics of defect parameters using the maximum temperature difference method.
Table 4.
Statistics of defect parameters using the maximum temperature difference method.
Defect Number | Frame | Threshold | Defect Pixel | Relative Error |
---|
#1 | 188 | 0.42 | 3525.5 | 4.52% |
#2 | 196 | 0.44 | 3191.9 | 4.42% |
Table 5.
Compared the results of MSDRM with those of maximum temperature difference method (MTDM) for PVC specimen.
Table 5.
Compared the results of MSDRM with those of maximum temperature difference method (MTDM) for PVC specimen.
Defect Number | MSDSRM | MTDM |
---|
Single Selection | Multiple Uniform Selection | Multiple Random Selection |
---|
Frame | Error | Frame | Error | Frame | Error | Frame | Error |
---|
#1 | (158, 193) | [0.25%, 4.94%] | 164 | 0.42% | (164, 173) | [0.24%, 1.06%] | 188 | 4.52% |
#2 | (160, 203) | [1.05%, 5.37%] | 182,183,186 | [0.94%, 2.44%] | (173, 193) | [0.47%, 3.15%] | 196 | 4.42% |
Table 6.
Comparison of the results of MSDRM and MTDM for CFRP specimen.
Table 6.
Comparison of the results of MSDRM and MTDM for CFRP specimen.
Method | Frame | Defect Pixel | Area (mm2) | Relative Error |
---|
MSDSRM | 164 | 6073.6 | 1765.17 | 3.91% |
MTDM | 304 | 3298.8 | 958.73 | 47.81% |