| Citation: |
AN Longhui, WANG Manli, ZHANG Changsen. Fault detection algorithm for underground conveyor belt deviation based on improved RT-DETR[J]. Journal of Mine Automation，2025，51（3）：54-62. DOI: 10.13272/j.issn.1671-251x.2024080089
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Current research on conveyor belt deviation detection mainly focuses on extracting the straight-line features of belt edges. The method requires setting specific thresholds and is easily affected by environmental factors, resulting in slow detection speed and low accuracy. To address the issue, an underground conveyor belt deviation fault detection algorithm based on an improved real-time detection transformer (RT-DETR) was proposed. The improved RT-DETR was used to directly detect a set of idlers and identify deviation based on the exposure degree of the left and right idlers. Three improvements were made to the RT-DETR backbone network: ① To reduce the number of parameters and floating-point operations (FLOPs), FasterNet Block was used to replace the BasicBlock in ResNet34. ② To enhance model accuracy and efficiency, the concept of structural reparameterization was introduced into the FasterNet Block structure. ③ To improve the feature extraction capability of FasterNet Block, an efficient multi-scale attention (EMA) Module was incorporated to capture both global and local feature maps more effectively. To expand the receptive field and capture more effective and comprehensive contextual information for richer feature representation, an improved high-level screening feature fusion pyramid network (HS-FPN) was adopted to optimize multi-scale feature fusion. Experimental results showed that compared to the baseline model, the improved RT-DETR reduced parameters and FLOPs by 8.4×106 and 17.8 G, respectively. The mAP@0.5 reached 94.5%, with a severe deviation detection accuracy of 99.2% and a detection speed of 41.0 frame per second, outperforming TOOD and ATSS object detection models, meeting the real-time and accuracy requirements of coal mine production.
| [1] |
CHU Qi,MENG Guoying,FAN Xun. Analysis of speed and belt deviation of the conveyor belt[J]. Advanced Materials Research,2011,339:444-447.
|
| [2] |
徐世昌,程刚,袁敦鹏,等. 基于三维点云的带式输送机跑偏及堆煤监测方法[J]. 工矿自动化,2022,48(9):8-15,24.
XU Shichang,CHENG Gang,YUAN Dunpeng,et al. Belt conveyor deviation and coal stacking monitoring method based on three-dimensional point cloud[J]. Journal of Mine Automation,2022,48(9):8-15,24.
|
| [3] |
ZHANG Mengchao,SHI Hao,YU Yan,et al. A computer vision based conveyor deviation detection system [J]. 2020,10(7). DOI:10.3390/ app10072402.
|
| [4] |
ZHANG Mengchao,JIANG Kai,CAO Yueshuai,et al. A deep learning-based method for deviation status detection in intelligent conveyor belt system[J]. Journal of Cleaner Production,2022,363. DOI:10.1016/j. jclepro.2022.132575.
|
| [5] |
XU Xinchao,ZHAO Hanguang,FU Xiaotian,et al. Real-time belt deviation detection method based on depth edge feature and gradient constraint[J]. Sensors,2023,23(19). DOI: 10.3390/s23198208.
|
| [6] |
WU Xiangfan,WANG Chusen,TIAN Zuzhi,et al. Research on belt deviation fault detection technology of belt conveyors based on machine vision[J]. Machines,2023,11(12). DOI: 10.3390/machines11121039.
|
| [7] |
LIU Yi,MIAO Changyun,LI Xianguo,et al. Research on deviation detection of belt conveyor based on inspection robot and deep learning[EB/OL]. [2024-06-10]. https://onlinelibrary.wiley.com/doi/10.1155/2021/3734560.
|
| [8] |
ZHAO Yian,LYU Wenyu,XU Shangliang,et al. DETRs beat YOLOs on real-time object detection[EB/OL]. [2024-06-10]. https://arxiv.org/html/2304.08069v3.
|
| [9] |
CHEN Jierun,KAO S H,HE Hao,et al. Run,don't walk:chasing higher FLOPS for faster neural networks[EB/OL]. [2024-06-10]. https://arxiv.org/abs/2303.03667v3.
|
| [10] |
DING Xiaohan,ZHANG Xiangyu,MA Ningning,et al. RepVGG:making VGG-style ConvNets great again[EB/OL]. [2024-06-10]. https://arxiv.org/abs/2101.03697.
|
| [11] |
OUYANG Daliang,HE Su,ZHAN Guozhong,et al. Efficient multi-scale attention module with cross-spatial learning[C]. IEEE International Conference on Acoustics,Speech and Signal Processing,Rhodes Island,2023:776-780.
|
| [12] |
CHEN Yifei,ZHANG Chenyan,CHEN Ben,et al. Accurate leukocyte detection based on deformable-DETR and multi-level feature fusion for aiding diagnosis of blood diseases[J]. Computers in Biology and Medicine,2024,170. DOI: 10.1016/j.compbiomed.2024.107917.
|
| [13] |
CAI Xinhao,LAI Qiuxia,WANG Yuwei,et al. Poly kernel inception network for remote sensing detection[EB/OL]. [2024-06-10]. https://arxiv.org/abs/2403.06258v2.
|
| [14] |
TAN Mingxing,PANG Ruoming,LE Q V,et al. EfficientDet:scalable and efficient object detection[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition,Seattle,2020:10778-10787.
|
| [15] |
FENG Chengjian,ZHONG Yujie,GAO Yu,et al. TOOD:task-aligned one-stage object detection[C]. IEEE/CVF International Conference on Computer Vision,Montreal,2021:3490-3499.
|
| [16] |
ZHANG Shifeng,CHI Cheng,YAO Yongqiang,et al. Bridging the gap between anchor-based and anchor-free detection via adaptive training sample selection[EB/OL]. [2024-06-10]. https://arxiv.org/abs/1912.02424v4.
|
| [17] |
ZHU Xizhou,SU Weijie,LU Lewei,et al. Deformable DETR:deformable transformers for end-to-end object detection[EB/OL]. [2024-06-10]. https://arxiv.org/pdf/2010.04159v1.
|
| [18] |
MENG Depu,CHEN Xiaokang,FAN Zejia,et al. Conditional DETR for fast training convergence[C]. IEEE/CVF International Conference on Computer Vision,Montreal,2021:3651-3660.
|
| [19] |
WANG C Y,BOCHKOVSKIY A,LIAO H Y M. YOLOv7:trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]. IEEE/CVF Conference on Computer Vision and Pattern Recognition,Vancouver,2023:7464-7475.
|
| [20] |
REIS D,KUPEC J,HONG J,et al. Real-time flying object detection with YOLOv8[EB/OL]. [2024-06-10]. https://arxiv.org/abs/2305.09972.
|
| [21] |
WANG C Y,YEH I H,MARK LIAO H Y M. YOLOv9:learning what you want toLearn using programmable gradient information[C]. European Conference on Computer Vision,Milan,2024:1-21.
|
| [22] |
唐俊,李敬兆,石晴,等. 基于Faster−YOLOv7的带式输送机异物实时检测[J]. 工矿自动化,2023,49(11):46-52,66.
TANG Jun,LI Jingzhao,SHI Qing,et al. Real time detection of foreign objects in belt conveyors based on Faster-YOLOv7[J]. Journal of Mine Automation,2023,49(11):46-52,66.
|
| [23] |
冯恒健,韩李涛,张鹏飞,等. 基于改进YOLOv7的高效行人检测方法[J]. 计算机应用,2024,44(增刊1):290-296.
FENG Hengjian,HAN Litao,ZHANG Pengfei,et al. Efficient Pedestrian Detection Method based on improved YOLOv7[J]. Journal of Computer Applications,2019,44(S1):290-296.
|
| [24] |
WANG Zhenyue,YUAN Guowu,ZHOU Hao,et al. Foreign-object detection in high-voltage transmission line based on improved YOLOv8m[J]. Applied Sciences,2023,13(23). DOI: 10.3390/app132312775.
|
| 1. |
赵安新, 黎梁, 栾兆彪, 叶磊, 姜鑫. 基于多元数据融合的钻孔岩性识别方法. 西安科技大学学报. 2025(03)
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