Intelligent monitoring method for conveyor belt misalignment based on deep learning
Existing methods for monitoring conveyor belt misalignment face challenges in terms of practicality, robustness, and the difficulty of dataset creation. This paper proposed an intelligent monitoring method for conveyor belt misalignment based on deep learning. First, the conveyor belt edge recogniti...
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| Format: | Article |
| Language: | zho |
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Editorial Department of Industry and Mine Automation
2024-12-01
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| Series: | Gong-kuang zidonghua |
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| Online Access: | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024030043 |
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| _version_ | 1832591117786284032 |
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| author | ZUO Mingming ZHANG Xi YANG Zihao SUN Qifei ZHANG Mengchao ZHANG Yuan LI Hu |
| author_facet | ZUO Mingming ZHANG Xi YANG Zihao SUN Qifei ZHANG Mengchao ZHANG Yuan LI Hu |
| author_sort | ZUO Mingming |
| collection | DOAJ |
| description | Existing methods for monitoring conveyor belt misalignment face challenges in terms of practicality, robustness, and the difficulty of dataset creation. This paper proposed an intelligent monitoring method for conveyor belt misalignment based on deep learning. First, the conveyor belt edge recognition problem was treated as a line detection issue in a specific scenario. A strategy was proposed to detect the straight lines of the conveyor belt edges using the diagonal features of the bounding box predicted by the object detection network. Specifically, the top-right to bottom-left diagonal of the predicted bounding box was used to represent the left edge of the conveyor belt, and the top-left to bottom-right diagonal was used to represent the right edge. The YOLOv5 model was employed to detect the conveyor belt edges, and a misalignment calculation method and misalignment state determination rules were developed. Experimental results demonstrated that the diagonal features of the predicted bounding box could stably and efficiently achieve conveyor belt edge recognition and misalignment quantification, thereby simplifying image data processing and annotation tasks. The method exhibited strong generalization ability and rapid transfer learning capability. The YOLOv5 model, combined with the line detection strategy, showed excellent anti-interference performance for detecting material flow boundaries, support pillars, and other straight lines. On the CUMT-BELT dataset, the detection accuracy exceeded 99%, with a maximum detection speed of 148 frames per second, ensuring excellent real-time performance. |
| format | Article |
| id | doaj-art-a810e37df8a448f7b3a5b5ab6a153414 |
| institution | Kabale University |
| issn | 1671-251X |
| language | zho |
| publishDate | 2024-12-01 |
| publisher | Editorial Department of Industry and Mine Automation |
| record_format | Article |
| series | Gong-kuang zidonghua |
| spelling | doaj-art-a810e37df8a448f7b3a5b5ab6a1534142025-01-23T02:17:44ZzhoEditorial Department of Industry and Mine AutomationGong-kuang zidonghua1671-251X2024-12-015012166172, 18210.13272/j.issn.1671-251x.2024030043Intelligent monitoring method for conveyor belt misalignment based on deep learningZUO Mingming0ZHANG Xi1YANG Zihao2SUN Qifei3ZHANG Mengchao4ZHANG YuanLI Hu5Shandong Zhaojin Group Company Limited, Zhaoyuan 265400, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaShandong Zhaojin Group Company Limited, Zhaoyuan 265400, ChinaCollege of Mechanical and Electronic Engineering, Shandong University of Science and Technology, Qingdao 266590, ChinaCCTEG Changzhou Research Institute, Changzhou 213015, ChinaExisting methods for monitoring conveyor belt misalignment face challenges in terms of practicality, robustness, and the difficulty of dataset creation. This paper proposed an intelligent monitoring method for conveyor belt misalignment based on deep learning. First, the conveyor belt edge recognition problem was treated as a line detection issue in a specific scenario. A strategy was proposed to detect the straight lines of the conveyor belt edges using the diagonal features of the bounding box predicted by the object detection network. Specifically, the top-right to bottom-left diagonal of the predicted bounding box was used to represent the left edge of the conveyor belt, and the top-left to bottom-right diagonal was used to represent the right edge. The YOLOv5 model was employed to detect the conveyor belt edges, and a misalignment calculation method and misalignment state determination rules were developed. Experimental results demonstrated that the diagonal features of the predicted bounding box could stably and efficiently achieve conveyor belt edge recognition and misalignment quantification, thereby simplifying image data processing and annotation tasks. The method exhibited strong generalization ability and rapid transfer learning capability. The YOLOv5 model, combined with the line detection strategy, showed excellent anti-interference performance for detecting material flow boundaries, support pillars, and other straight lines. On the CUMT-BELT dataset, the detection accuracy exceeded 99%, with a maximum detection speed of 148 frames per second, ensuring excellent real-time performance.http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024030043belt conveyormisalignment monitoringline detectionconveyor belt edge recognitiondeep learningyolov5 |
| spellingShingle | ZUO Mingming ZHANG Xi YANG Zihao SUN Qifei ZHANG Mengchao ZHANG Yuan LI Hu Intelligent monitoring method for conveyor belt misalignment based on deep learning Gong-kuang zidonghua belt conveyor misalignment monitoring line detection conveyor belt edge recognition deep learning yolov5 |
| title | Intelligent monitoring method for conveyor belt misalignment based on deep learning |
| title_full | Intelligent monitoring method for conveyor belt misalignment based on deep learning |
| title_fullStr | Intelligent monitoring method for conveyor belt misalignment based on deep learning |
| title_full_unstemmed | Intelligent monitoring method for conveyor belt misalignment based on deep learning |
| title_short | Intelligent monitoring method for conveyor belt misalignment based on deep learning |
| title_sort | intelligent monitoring method for conveyor belt misalignment based on deep learning |
| topic | belt conveyor misalignment monitoring line detection conveyor belt edge recognition deep learning yolov5 |
| url | http://www.gkzdh.cn/article/doi/10.13272/j.issn.1671-251x.2024030043 |
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