In recent years, railway accidents caused by railway perimeter disasters have occurred frequently. The development of deep learning target detection algorithm and the improvement of the picture quality of railway perimeter cameras make it possible to use video images to monitor railway perimeter disasters. Based on this, this paper proposes a railway perimeter disaster image monitoring method based on YOLOV4_Lite, aiming to replace the manual inspection with high risk.

    In this paper, the YOLOV4_Lite model was trained by using the produced disaster data set, and the training results were compared with the YOLOV4 model. The results show that the number of parameters of YOLOV4_Lite algorithm is about 20% of that of YOLOV4 model, the processing speed of YOLOV4_Lite algorithm is 3.2 times of that of YOLOV4 model, and the detection accuracy of perimeter disasters is also slightly improved. In contrast, the YOLOV4_Lite model is more suitable for railway perimeter disaster monitoring scenarios.


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    Title :

    Railway Perimeter Disaster Image Monitoring Method Based on YOLOV4_Lite


    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) / Ren, Weitao (author) / Xie, Zhengyu (author) / Qin, Yong (author) / Lin, Feng (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 :

    8 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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