Traffic sign detection and recognition is an important task in the perception of intelligent vehicles (IVs). Among various categories of traffic signs, prohibition signs are of paramount importance. However, there is relatively little research on the detection and recognition of prohibition signs at present. In this study, a modified lightweight model based on YOLOV5s is proposed for detecting the 12 most common types of Chinese prohibition signs. A new dataset is established by collecting images of Chinese prohibition traffic signs in real-world scenarios. The improved model replaces the normal convolution with ghost convolution in the feature fusion network, which greatly reduces the number of parameters and computational complexity. The introduction of the coordinate attention mechanism in the feature extraction network helps the network to better learn and leverage the position information of the target on the feature map. The Mosaic data augmentation strategy at the input end is refined by increasing the number of concatenated images from 4 to 9, allowing the network to learn a richer representation of the scene and target features. The experimental results on the self-built dataset demonstrate that the proposed algorithm, compared to YOLOV5s, achieves a significant reduction in model parameters of 28.4%, model size of 27.5%, and computational cost of 27% while only experiencing a slight decrease in accuracy of 0.3%. Further comparative experiment results show that compared with current mainstream lightweight algorithms, our proposed model achieves a better balance between lightweight and accuracy.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    An Improved Detection Method of Traffic Prohibition Sign for Intelligent Vehicles based on YOLOV5s


    Contributors:
    Xiao, Zhihao (author) / Chen, Wei (author) / Du, Luyao (author) / Yin, Tianrui (author) / Tong, Bingming (author) / Su, Zixu (author)


    Publication date :

    2023-08-04


    Size :

    1637384 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    A YOLOv5s-based Method for Intelligent Pedestrian Detection in Vehicles

    Feng, Bin / Wang, Zichen / Bi, Wanglinfeng | IEEE | 2024


    Intelligent traffic road condition violation prohibition sign storage device

    WANG JIANGTAO | European Patent Office | 2015

    Free access

    Night traffic flow detection method based on ShuffleNetv2-YOLOv5s

    CHEN DONG / CHEN MAO | European Patent Office | 2024

    Free access

    UAV target detection algorithm based on improved YOLOv5s

    Zhang, Tao / Wang, Fenmei / Chen, Dongxu et al. | SPIE | 2023


    Research on Vehicle Detection Method Based on Improved YOLOv5s

    Ma, Liangliang / Zhong, Runlu / Shi, Xiaohong et al. | IEEE | 2024