With the rise in the number of vehicles on the streets, urban road problems are becoming more and more prominent. As the vehicle of the road subject, it is the subject of the problem of intelligent transportation system. In this paper, we discuss the vehicle detection problem in intelligent transportation system, and propose a lightweight YOLOv5 model combining SENet attention mechanism and depthwise separable convolution for the complex situation of vehicles in street video, such as vehicle occlusion, vehicle misdetection and vehicle omission. The feasibility and effectiveness of the method are verified by testing on UA-DETRAC and self-built vehicle dataset.


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    Title :

    Road Traffic Vehicle Detection Method Using Lightweight YOLOv5 and Attention Mechanism


    Contributors:


    Publication date :

    2022-07-26


    Size :

    4963573 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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