Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
The Yolov4 detection algorithm does not sufficiently extract local semantic and location information. This study aims to solve this problem by proposing a Yolov4-based multiscale feature fusion detection system for high-speed train wheel tread defects. First, multiscale feature maps are obtained from a feature extraction backbone network. The proposed multiscale feature fusion network then fuses the underlying features of the original three scales. These fused features contain more defect semantic information and location details. Based on the fused features, a path aggregation network is used to fuse feature maps at different resolutions, with an improved loss function that speeds up the convergence of the network. Experimental results show that the proposed method is effective at detecting defects in the wheel treads of high-speed trains.
- Record URL:
-
Availability:
- Find a library where document is available. Order URL: http://worldcat.org/oclc/5121625
-
Supplemental Notes:
- © 2022 Changfan Zhang et al.
-
Authors:
- Zhang, Changfan
- Hu, Xinliang
- He, Jing
- Hou, Na
- Publication Date: 2022-3
Language
- English
Media Info
- Media Type: Web
- Features: Figures; References; Tables;
- Pagination: Article ID 1172654
-
Serial:
- Journal of Advanced Transportation
- Volume: 2022
- Publisher: John Wiley & Sons, Incorporated
- ISSN: 0197-6729
- EISSN: 2042-3195
- Serial URL: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2042-3195
-
Publication flags:
Open Access (libre)
Subject/Index Terms
- TRT Terms: Defects; Detection and identification systems; High speed rail; Railroad wheelsets
- Identifier Terms: YOLO
- Subject Areas: Railroads; Vehicles and Equipment;
Filing Info
- Accession Number: 01843407
- Record Type: Publication
- Files: TRIS
- Created Date: Apr 25 2022 10:06AM