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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Main Authors: Changfan Zhang, Xinliang Hu, Jing He, Na Hou
Format: Article
Language:English
Published: Wiley 2022-01-01
Series:Journal of Advanced Transportation
Online Access:http://dx.doi.org/10.1155/2022/1172654
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author Changfan Zhang
Xinliang Hu
Jing He
Na Hou
author_facet Changfan Zhang
Xinliang Hu
Jing He
Na Hou
author_sort Changfan Zhang
collection DOAJ
description 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.
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id doaj-art-2fb9e45b7ead4e22b6fa42c1aa2796ef
institution DOAJ
issn 2042-3195
language English
publishDate 2022-01-01
publisher Wiley
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series Journal of Advanced Transportation
spelling doaj-art-2fb9e45b7ead4e22b6fa42c1aa2796ef2025-08-20T03:20:24ZengWileyJournal of Advanced Transportation2042-31952022-01-01202210.1155/2022/1172654Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature FusionChangfan Zhang0Xinliang Hu1Jing He2Na Hou3School of Electrical and Information EngineeringSchool of Railway TransportationSchool of Electrical and Information EngineeringSchool of Electrical and Information EngineeringThe 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.http://dx.doi.org/10.1155/2022/1172654
spellingShingle Changfan Zhang
Xinliang Hu
Jing He
Na Hou
Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
Journal of Advanced Transportation
title Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
title_full Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
title_fullStr Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
title_full_unstemmed Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
title_short Yolov4 High-Speed Train Wheelset Tread Defect Detection System Based on Multiscale Feature Fusion
title_sort yolov4 high speed train wheelset tread defect detection system based on multiscale feature fusion
url http://dx.doi.org/10.1155/2022/1172654
work_keys_str_mv AT changfanzhang yolov4highspeedtrainwheelsettreaddefectdetectionsystembasedonmultiscalefeaturefusion
AT xinlianghu yolov4highspeedtrainwheelsettreaddefectdetectionsystembasedonmultiscalefeaturefusion
AT jinghe yolov4highspeedtrainwheelsettreaddefectdetectionsystembasedonmultiscalefeaturefusion
AT nahou yolov4highspeedtrainwheelsettreaddefectdetectionsystembasedonmultiscalefeaturefusion