Damage Detection Method for High-Strength Aramid Conveyor Belts
Mining conveyor belts play a crucial role in the mining transportation system, and the detection of damage caused to them can affect productivity and safety. Since the traditional methods of detection perform poorly in efficiency and accuracy, a new method of damage detection is proposed based on th...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10807289/ |
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| _version_ | 1841557055734808576 |
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| author | Ling Yang Yimin Wang Di Miao Xiaoxian Duan |
| author_facet | Ling Yang Yimin Wang Di Miao Xiaoxian Duan |
| author_sort | Ling Yang |
| collection | DOAJ |
| description | Mining conveyor belts play a crucial role in the mining transportation system, and the detection of damage caused to them can affect productivity and safety. Since the traditional methods of detection perform poorly in efficiency and accuracy, a new method of damage detection is proposed based on the FCSA - YOLOv10 model in this paper for high-strength aramid conveyor belts. This method is used to capture the image of aramid conveyor belts by irradiating the conveyor belt with X-rays, and the FCSA-YOLOv10 model is applied to damage detection. By introducing the Faster structure, the C2f structure is improved. Also, the Convolutional Block Attention Module (CBAM) is used to replace the Multi-head Self-Attention (MHSA) in the PSA structure, while the SimSPPFCSPC module is used to replace the original SPPF structure. Meanwhile, the Adaptive Threshold Focal Loss Function (ATFL) is applied to address the imbalance between the background and the target in images of the aramid conveyor belt. Experimental results show that the FCSA-YOLOv10 model significantly improves the accuracy of detection, recall rate, and precision on the dataset of mining aramid conveyor belt. Specifically, they reach 98.5%, 95.1%, and 96.7%, respectively. To sum up, this research contributes an effective solution to the efficient detection of damage caused to high-strength aramid conveyor belts. |
| format | Article |
| id | doaj-art-e4b925c1cea8440b83c260af7ca542d8 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-e4b925c1cea8440b83c260af7ca542d82025-01-07T00:02:24ZengIEEEIEEE Access2169-35362025-01-01132671267810.1109/ACCESS.2024.352020410807289Damage Detection Method for High-Strength Aramid Conveyor BeltsLing Yang0Yimin Wang1https://orcid.org/0000-0003-4455-9212Di Miao2https://orcid.org/0000-0001-8235-8345Xiaoxian Duan3School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Intelligent Manufacturing (IM), TianjinSino-German University of Applied Sciences, Tianjin, ChinaSchool of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Electronic Engineering, Tianjin University of Technology and Education, Tianjin, ChinaMining conveyor belts play a crucial role in the mining transportation system, and the detection of damage caused to them can affect productivity and safety. Since the traditional methods of detection perform poorly in efficiency and accuracy, a new method of damage detection is proposed based on the FCSA - YOLOv10 model in this paper for high-strength aramid conveyor belts. This method is used to capture the image of aramid conveyor belts by irradiating the conveyor belt with X-rays, and the FCSA-YOLOv10 model is applied to damage detection. By introducing the Faster structure, the C2f structure is improved. Also, the Convolutional Block Attention Module (CBAM) is used to replace the Multi-head Self-Attention (MHSA) in the PSA structure, while the SimSPPFCSPC module is used to replace the original SPPF structure. Meanwhile, the Adaptive Threshold Focal Loss Function (ATFL) is applied to address the imbalance between the background and the target in images of the aramid conveyor belt. Experimental results show that the FCSA-YOLOv10 model significantly improves the accuracy of detection, recall rate, and precision on the dataset of mining aramid conveyor belt. Specifically, they reach 98.5%, 95.1%, and 96.7%, respectively. To sum up, this research contributes an effective solution to the efficient detection of damage caused to high-strength aramid conveyor belts.https://ieeexplore.ieee.org/document/10807289/Aramid conveyor beltnon-destructive testingX-rayFCSA-YOLOv10SimSPPFCSPC module |
| spellingShingle | Ling Yang Yimin Wang Di Miao Xiaoxian Duan Damage Detection Method for High-Strength Aramid Conveyor Belts IEEE Access Aramid conveyor belt non-destructive testing X-ray FCSA-YOLOv10 SimSPPFCSPC module |
| title | Damage Detection Method for High-Strength Aramid Conveyor Belts |
| title_full | Damage Detection Method for High-Strength Aramid Conveyor Belts |
| title_fullStr | Damage Detection Method for High-Strength Aramid Conveyor Belts |
| title_full_unstemmed | Damage Detection Method for High-Strength Aramid Conveyor Belts |
| title_short | Damage Detection Method for High-Strength Aramid Conveyor Belts |
| title_sort | damage detection method for high strength aramid conveyor belts |
| topic | Aramid conveyor belt non-destructive testing X-ray FCSA-YOLOv10 SimSPPFCSPC module |
| url | https://ieeexplore.ieee.org/document/10807289/ |
| work_keys_str_mv | AT lingyang damagedetectionmethodforhighstrengtharamidconveyorbelts AT yiminwang damagedetectionmethodforhighstrengtharamidconveyorbelts AT dimiao damagedetectionmethodforhighstrengtharamidconveyorbelts AT xiaoxianduan damagedetectionmethodforhighstrengtharamidconveyorbelts |