The subway has the advantage of convenience and speed in our daily life, which brings convenience to our traffic. At the same time, the high load operation behind this has brought huge challenges and pressures to safety issues. In view of track safety issues, people have proposed some detection methods, such as fasteners defect detection, rail crack detection, sleeper broken detection, sensor board shift detection, and foreign object detection. Vision-based recognition methods such as Haar-Like + Adboost, HOG + SVM, and the use of laser sensors are often not particularly ideal. Therefore, improvements have been made on the basis of yolov3 target detection algorithm, and a multi-level feature map cascade based on feature pyramid is proposed. The Densely connected feature map performs the task of identifying each sub-target of the defect, and finally realizes the recognition of high precision and frame rate on the 3080Ti deep learning platform.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Track Defect Recognition Algorithm Based on Deep Learning


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Qin, Yong (editor) / Jia, Limin (editor) / Liang, Jianying (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Fang, Enquan (author) / Yang, Kunshan (author) / Zeng, Ming (author)

    Conference:

    International Conference on Electrical and Information Technologies for Rail Transportation ; 2021 October 21, 2021 - October 23, 2021



    Publication date :

    2022-02-23


    Size :

    12 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




    Track Defect Recognition Algorithm Based on Deep Learning

    Fang, Enquan / Yang, Kunshan / Zeng, Ming | British Library Conference Proceedings | 2022


    Track Defect Recognition Algorithm Based on Deep Learning

    Fang, Enquan / Yang, Kunshan / Zeng, Ming | TIBKAT | 2022



    Deep Learning-Based Image Analysis to Track Vehicles

    Prabakaran, P / Jayasudha, R. / Sandhu, Mukta et al. | IEEE | 2024


    Research on Traffic Target Recognition Algorithm Based on Deep Learning

    Li, Dan / Dang, Xiangying / Shi, Hanqin et al. | IEEE | 2023