Most of the current vehicle detection models based on depth learning have many problems such as large parameters, high computational complexity, and most lightweight target detection models can not meet the actual work requirements in accuracy. Aiming at this problem, this paper proposes a vehicle detection method based on improved YOLOv3. In order to reduce model parameters and speed up network inference, CSPNet and Ghost modules are added to the Backbone part of YOLOv3; Expand the feature input size of the feature fusion network, add the interval fusion structure of four times of up sampling on the basis of the original two times of up sampling, and use Depth Separable Convolution instead of conventional convolution to further compress the model parameters. The experimental results show that the accuracy of the improved model is 1.5% higher than that before the improvement, the detection speed is 23.0ms faster, and the model has good robustness, which can meet the detection requirements of different scenes.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle detection method based on improved YOLOv3


    Contributors:
    Qi, Cheng (author) / Shen, Xizhong (author)


    Publication date :

    2022-10-12


    Size :

    1353825 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    SAR SHIP DETECTION BASED ON IMPROVED YOLOV3

    Chen, D. / Ju, Y. | TIBKAT | 2021


    Non-motor vehicle helmet wearing detection method based on improved YOLOv3 algorithm

    ZHENG SHUIHUA / XU YILUN / SUN ZENAN et al. | European Patent Office | 2021

    Free access

    Vehicle Detection Method Based on ADE-YOLOV3 Algorithm

    Yunxiang Liu, Guoqing Zhang, Yuanyuan Zhang: SIT, Shanghai | BASE | 2020

    Free access

    Remote Sensing Object Detection Based on Improved YOLOv3

    Dong, Wenlong / Nie, Shiyang / Wang, Yibo | IEEE | 2022


    Improving Performance of YOLOv3 for Vehicle Detection

    Prihatmaja, Pratamamia A. / Widyantoro, Dwi H. | IEEE | 2019