Rapid detection of wheel tread defects for YOLO-v5 trains based on residual attention
In respect of large noise interference of wheel tread and insufficient feature fusion of traditional detection algorithms, in order to achieve fast and accurate detection of wheel tread defects, a method for rapid detection of wheel tread defects for YOLO-v5 trains based on residual attention was pr...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | zho |
| Published: |
Editorial Department of Electric Drive for Locomotives
2022-11-01
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| Series: | 机车电传动 |
| Subjects: | |
| Online Access: | http://edl.csrzic.com/thesisDetails#10.13890/j.issn.1000-128X.2022.06.001 |
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| Summary: | In respect of large noise interference of wheel tread and insufficient feature fusion of traditional detection algorithms, in order to achieve fast and accurate detection of wheel tread defects, a method for rapid detection of wheel tread defects for YOLO-v5 trains based on residual attention was proposed. First, for the large noise interference, a residual attention noise reduction module was designed to effectively improve the accuracy of model detection, and the Grad-CAM activation mapping technology was used to verify the effect of the residual attention module on reducing noise interference. Secondly, in view of insufficient feature fusion and the model being prone to omission, a feature fusion module of bidirectional feature pyramid was used to efficiently fuse the features extracted by the backbone network, thereby effectively reducing the omission rate. Finally, hundreds of real defect images of wheel treads were collected and contrasted with five classic detection models to verify the superiority of the algorithm. The results show that the algorithm can achieve 77.9% accuracy and 72.3% recall; in addition, the proposed algorithm can detect 125 images per second, and the model weight is only 15.1 MB. The model can quickly and accurately detect such two defects as peeling and sagging, and can be easily applied to the actual real-time defect detection scenario of wheel tread. |
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| ISSN: | 1000-128X |