Self-driving cars have recently gained in popularity. This is because of rapid advances in vehicle and artificial intelligence technology. Autonomous cars’ ability to drive effectively and safely depends heavily on their capacity to recognize traffic signs. Traditional visual recognition of things, conversely, relies heavily on the extraction of visual features, such as color and edge. Despite these efforts, the varying appearance of road signs across geographical areas, lighting changes, and complex background situations continues to prevent the development of accurate traffic sign recognition platforms. In this paper, we present YOLO-TSR, a novel network based on YOLOv8 that innovatively tackles the challenges encountered in road sign recognition (TSR). Our intention is to provide a method to detect and recognize traffic signs in complex situations and under varying weather conditions. The proposed method was validated against three separate traffic sign datasets: our privately curated dataset, the widely recognized German Traffic Sign Recognition Benchmark (GTSRB) dataset, and the Belgium Traffic Sign Dataset. We conducted numerous experiments to validate the proposed algorithm’s effectiveness. The proposed algorithm achieves 98.79% accuracy, 92.18% recall, 96.21% mAP@0.5, and 84.32% mAP@0.5:0.95 for the GTSRB dataset. For our private dataset, the algorithm had an accuracy of 96.62%, a recall of 90.81%, mAP@0.5 of 94.83%, and mAP@0.5:0.95 of 81.70%. Furthermore, the algorithm maintains a consistent frame rate of 73 frames per second, which meets real-time detection requirements.


    Access

    Download

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    YOLO-TSR: A Novel YOLOv8-Based Network for Robust Traffic Sign Recognition


    Additional title:

    Transportation Research Record: Journal of the Transportation Research Board


    Contributors:


    Publication date :

    2025-04-04




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English



    Yolo-Based Traffic Sign Recognition Algorithm

    Ming Li / Li Zhang / Linlin Li et al. | DOAJ | 2022

    Free access

    Traffic sign recognition system based on YOLOv8-ConvNeXt

    Qin, Lijuan / Tang, Xiaoyu / Fan, Chubin | SPIE | 2025


    Enhanced Traffic Sign Recognition Using Advanced YOLOv8 Model

    Choudhary, Nishant / Sharma, Rishabh / Upadhyay, Deepak et al. | IEEE | 2024


    Research on traffic sign recognition based on the YOLOv8 algorithm

    Qi, Yinpeng / Ni, Hongxia / Feng, Siliang et al. | SPIE | 2024


    Traffic Sign Detection Using YOLOv8

    Kumar, Rahul / Gupta, Aniket / D, Rajeswari | IEEE | 2024