With the rapid development of my country's railway industry, no defects on the track surface are a prerequisite to ensure the normal operation of the train. Using deep learning instead of humans to detect defects on track surfaces can greatly improve the efficiency of detection while ensuring the safety of workers. However, in real life, railway workers will quickly repair and replace once the rail fails. Although this measure ensures the smooth operation of the train, it will bring difficulties to the collection of datasets, resulting in the lack of data set images of tracks with surface defects. In this paper, Gan is used to extend the dataset and then EfficientDet algorithm is used to detect and recognize the defects on the track surface. By comparing the efficiency and accuracy of the efficientD0, D3 and D6 algorithms, the final one was EfficientDet-D3. the experiment showed that the D3 version could achieve faster detection speed while ensuring the accuracy.


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    Title :

    Track Surface Defect Detection Based on EfficientDet


    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) / Xu, Yunhua (author) / Wang, Xuejun (author) / Li, Shuyuan (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 :

    11 pages





    Type of media :

    Article/Chapter (Book)


    Type of material :

    Electronic Resource


    Language :

    English




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