In the field of autonomous driving, a key requirement is the fast and accurate identification of vehicles on the road. While many researchers have conducted extensive research on vehicle detection in general road environments using deep learning techniques, there are still shortcomings in vehicle detection under certain special driving conditions. In this study, the manually processed datasets were used to train and test the YOLOv5s model, which is based on a single-stage object recognition algorithm. The influence of the datasets on the recognition performance of the model was confirmed by the test results. The recognition model was trained twice. In the first training, the specially processed datasets were not included in the training process, resulting in a final accuracy of 89%. In the second training, the processed datasets were included in the training process, resulting in a lower accuracy of 87%, and the second trained model was able to quickly and accurately identify vehicles even in the presence of noise interference or incomplete vehicle images.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Vehicle Recognition under Autonomous Driving Based on YOLOv5


    Contributors:


    Publication date :

    2024-05-24


    Size :

    2526082 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Pedestrian Detection with YOLOv5 in Autonomous Driving Scenario

    Jin, Xianjian / Li, Zhiwei / Yang, Hang | IEEE | 2021


    Pedestrian Detection Using YOLOv5 For Autonomous Driving Applications

    Vikram Reddy, Etikala Raja / Thale, Sushil | IEEE | 2021


    Traffic sign recognition based on YOLOv5

    Hou, Fujin / huo, Yanqiang / Lu, Youfu et al. | SPIE | 2022


    Apple Recognition Algorithm Based on YOLOv5

    Xu, Wenzhe / Yao, Ziqian / Zhou, Xuelin et al. | IEEE | 2024


    Ship Remote Sensing Target Recognition Based on YOLOV5

    Hao, Ning / Li, Yunwei / Ma, Yusen et al. | Springer Verlag | 2024