A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model
As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and wi...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Technologies |
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| Online Access: | https://www.mdpi.com/2227-7080/11/5/114 |
| _version_ | 1797572166276874240 |
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| author | Rongbin Yao Peng Qi Dezheng Hua Xu Zhang He Lu Xinhua Liu |
| author_facet | Rongbin Yao Peng Qi Dezheng Hua Xu Zhang He Lu Xinhua Liu |
| author_sort | Rongbin Yao |
| collection | DOAJ |
| description | As one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and <i>mAP</i><sup>50</sup> of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms. |
| first_indexed | 2024-03-10T20:50:52Z |
| format | Article |
| id | doaj.art-319fc4025e0840808c19b3e77c7f1b38 |
| institution | Directory Open Access Journal |
| issn | 2227-7080 |
| language | English |
| last_indexed | 2024-03-10T20:50:52Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Technologies |
| spelling | doaj.art-319fc4025e0840808c19b3e77c7f1b382023-11-19T18:20:03ZengMDPI AGTechnologies2227-70802023-08-0111511410.3390/technologies11050114A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX ModelRongbin Yao0Peng Qi1Dezheng Hua2Xu Zhang3He Lu4Xinhua Liu5Lianyungang Normal College, Lianyungang 222006, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaLianyungang Normal College, Lianyungang 222006, ChinaSchool of Mechatronic Engineering, China University of Mining and Technology, Xuzhou 221116, ChinaAs one of the main pieces of equipment in coal transportation, the belt conveyor with its detection system is an important area of research for the development of intelligent mines. Occurrences of non-coal foreign objects making contact with belts are common in complex production environments and with improper human operation. In order to avoid major safety accidents caused by scratches, deviation, and the breakage of belts, a foreign object detection method is proposed for belt conveyors in this work. Firstly, a foreign object image dataset is collected and established, and an IAT image enhancement module and an attention mechanism for CBAM are introduced to enhance the image data sample. Moreover, to predict the angle information of foreign objects with large aspect ratios, a rotating decoupling head is designed and a MO-YOLOX network structure is constructed. Some experiments are carried out with the belt conveyor in the mine’s intelligent mining equipment laboratory, and different foreign objects are analyzed. The experimental results show that the accuracy, recall, and <i>mAP</i><sup>50</sup> of the proposed rotating frame foreign object detection method reach 93.87%, 93.69%, and 93.68%, respectively, and the average inference time for foreign object detection is 25 ms.https://www.mdpi.com/2227-7080/11/5/114belt conveyorforeign object detectionYOLOXimage enhancementrotation detection |
| spellingShingle | Rongbin Yao Peng Qi Dezheng Hua Xu Zhang He Lu Xinhua Liu A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model Technologies belt conveyor foreign object detection YOLOX image enhancement rotation detection |
| title | A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model |
| title_full | A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model |
| title_fullStr | A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model |
| title_full_unstemmed | A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model |
| title_short | A Foreign Object Detection Method for Belt Conveyors Based on an Improved YOLOX Model |
| title_sort | foreign object detection method for belt conveyors based on an improved yolox model |
| topic | belt conveyor foreign object detection YOLOX image enhancement rotation detection |
| url | https://www.mdpi.com/2227-7080/11/5/114 |
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