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 fro...

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Bibliographic Details
Published inJournal of advanced transportation Vol. 2022; pp. 1 - 13
Main Authors Zhang, Changfan, Hu, Xinliang, He, Jing, Hou, Na
Format Journal Article
LanguageEnglish
Published London Hindawi 27.03.2022
Hindawi Limited
Wiley
Subjects
Algorithms
Classification
Computer networks
Datasets
Deep learning
Defects
Detectors
Fault diagnosis
Feature extraction
Feature maps
High speed rail
High speed trains
Locomotives
Methods
Neural networks
Noise
Railroad wheels
Railroads
Semantics
Telecommunication systems
Trains
Transportation
Treads
Vision systems
Wheelsets
Working conditions
China
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Summary: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.
ISSN:0197-6729
2042-3195
DOI:10.1155/2022/1172654