*4.2. The Processing Result Analysis of Our Method*

Images from groups 2 to 4 were processed by using both our method and manual segmentation. Three mixed material images include one fine material image from group 2 and two mixed material images from groups 3 and 4. The results of the three processed empty belt images are shown in Table 2. The results of the three processed mixed material images are shown in Table 3. The results of the three processed coarse material images are shown in Table 4. The cumulative area distribution of one fine material image from group 2, one mixed material image from group 3, and one coarse material image from group 2, which were processed by our method, are shown in Figure 11. The column headers in Tables 3 and 4 (5000, 10,000, etc.) indicate area filters. Table 2 shows that three empty belt images used for testing were alarmed. Our method is found to be accurate and reliable for empty belt identification. From Table 3, it is observed that the maximum error of the cumulative area distribution calculated by using different area filters is 2.71% for fine material, 4.65% for mixed materials from group 3, and 5.02% for mixed materials from group 4. From Table 3, the average error of the cumulative area distribution calculated by using different area filters is 1.07% for fine material, 2.27% for mixed materials from group 3, and 2.89% for mixed materials from group 4. From Table 4, it is observed that the maximum error of the cumulative area distribution calculated by using different area filters is 3.51%, 5.61%, 3.83%, respectively, for coarse materials from groups 2 to 4. From Table 4, the average error of the cumulative area distribution calculated by using different area filters is 1.30%, 3.30%, 2.59%, respectively, for coarse materials from groups 2 to 4. Tables 3 and 4 indicate that our method can segment both mixed material images and coarse material images with high precision. Our method is considered useful for identifying and segmenting the images taken on industrial conveyor belts.


**Table 3.** The results of mixed material images from groups 2 to 4, processed by our method.


**Table 4.** The results of coarse material images from groups 2 to 4, processed by our method.


**Figure 11.** The results of the specified images processed by our method: fine material (**a**), mixed **Figure 11.** The results of the specified images processed by our method: fine material (**a**), mixed materials (**b**), and coarse materials (**c**).

#### **5. Conclusions**

materials (**b**), and coarse materials (**c**).

**5. Conclusions**  The objective of this study is to develop a method that can accurately segment belt ore images. The accurate image segmentation method is considered important for estimating the size distribution of mineral materials on industrial conveyor belts. For that purpose, 2880 images collected from a process control system on the industrial site were processed as the original dataset. Deep learning and image processing techniques were integrated with the image segmentation method. From the perspective of an application, the accurate recognition of the empty belts is necessary to realize the automatic switch of conveyor belts. The new method can identify the empty belt images with high precision. Moreover, it is difficult for an image segmentation algorithm to achieve accurate segmentation of both coarse materials and mixed materials. This study adopted the convolutional neural network model to solve the automatic classification of belt ore images, and then used the CIS The objective of this study is to develop a method that can accurately segment belt ore images. The accurate image segmentation method is considered important for estimating the size distribution of mineral materials on industrial conveyor belts. For that purpose, 2880 images collected from a process control system on the industrial site were processed as the original dataset. Deep learning and image processing techniques were integrated with the image segmentation method. From the perspective of an application, the accurate recognition of the empty belts is necessary to realize the automatic switch of conveyor belts. The new method can identify the empty belt images with high precision. Moreover, it is difficult for an image segmentation algorithm to achieve accurate segmentation of both coarse materials and mixed materials. This study adopted the convolutional neural network model to solve the automatic classification of belt ore images, and then used the CIS algorithm and the FIS algorithm to accurately segment coarse material images and mixed material images, respectively. The new method

algorithm and the FIS algorithm to accurately segment coarse material images and mixed material images, respectively. The new method makes it feasible and efficient to accurately process various makes it feasible and efficient to accurately process various belt ore images. Notably, it can be used as a brand new algorithm for belt ore image processing. The main novelties are as follows:


**Author Contributions:** Conceptualization, X.M. (Xiqi Ma) and L.O.; methodology, X.M. (Xiqi Ma); software, X.M. (Xiqi Ma) and P.Z.; validation, X.M. (Xiqi Ma) and X.M. (Xiaofei Man); formal analysis, X.M. (Xiqi Ma) and P.Z.; investigation, X.M. (Xiqi Ma); resources, L.O.; data curation, X.M. (Xiqi Ma); writing—original draft preparation, X.M. (Xiqi Ma), X.M. (Xiaofei Man) and P.Z.; writing—review and editing, X.M. (Xiqi Ma), P.Z. and L.O.; supervision, L.O. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was financially supported by the National Natural Science Foundation of China (No. 51674291).

**Acknowledgments:** The authors also thank the support of the Key Laboratory of Hunan Province for Clean and Efficient Utilization of Strategic Calcium-containing Mineral Resources (No. 2018TP1002). Special thanks to Wencai Zhang for his suggestions on paper writing.

**Conflicts of Interest:** The authors declare no conflict of interest.
