In recent years, high-speed railways have developed rapidly in China, but the detection of perimeter intrusion object is still difficult. Based on a brief introduction of the characteristics of small object detection, this article combines the development status of object detection algorithms based on convolutional neural networks, and relies on self-made high-speed railway datasets to compare and analyze some representative object detection algorithms. Experiments show that among the algorithms selected in this article, YOLOv5 has the best overall performance, reaching 68.1% mAP and 31 FPS. PP-YOLOv2 has the highest detection accuracy, YOLOv4 is the fastest one, and YOLOv5 has the lowest missing rate. The experimental results have good reference, and have a certain contribution to the detection of small object in the perimeter of high-speed railways.


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    Title :

    Comparison on Detection Algorithms of Small Object Intrusion on High-Speed Railway


    Additional title:

    Lect. Notes Electrical Eng.


    Contributors:
    Liang, Jianying (editor) / Jia, Limin (editor) / Qin, Yong (editor) / Liu, Zhigang (editor) / Diao, Lijun (editor) / An, Min (editor) / Pan, Fan (author) / Xie, Zhengyu (author) / Qin, Yong (author) / Ma, Xiaoning (author)

    Conference:

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



    Publication date :

    2022-02-19


    Size :

    9 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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