At present, the development of our country is getting better and better, the vehicles running on the road are also increasing, so the traffic problems are becoming more and more obvious. This kind of problem will also set up the development of the modern city. At this time, the intelligent transportation technology has also developed, and the above problems are gradually treated by new methods. It has become one of the hot topics in the field of an intelligent transportation system to use the advantages of machine learning technology to deal with traffic congestion and improve the traffic efficiency of the road network. It has high theoretical and practical significance to detect road traffic signs in the actual scene. A method based on directional gradient histogram features combined with a support vector machine classifier is proposed. Each type of traffic sign has its own characteristics. By classifying its appearance and color, many recognition methods are produced, and the target area is retained by a unique method, thus the feature can be extracted and identified. Make the paving. The main work is to obtain a training sample, and then add the direction gradient histogram of the sample library into the SVM for training, to get a one to many classifiers to be tuned continuously, it can realize the rapid and accurate judgment of multiple traffic signs.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Traffic Sign Recognition Algorithm Model Based on Machine Learning


    Weitere Titelangaben:

    Lect.Notes Social.Inform.


    Beteiligte:
    Li, Wuyungerile (Herausgeber:in) / Tang, Dalai (Herausgeber:in) / Li, Hui (Autor:in) / Feng, Jun (Autor:in) / Liu, Jialing (Autor:in) / Gong, Yanli (Autor:in)

    Kongress:

    International Conference on Mobile Wireless Middleware, Operating Systems, and Applications ; 2020 ; Hohhot, China July 11, 2020 - July 11, 2020



    Erscheinungsdatum :

    05.11.2020


    Format / Umfang :

    13 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Yolo-Based Traffic Sign Recognition Algorithm

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

    Freier Zugriff

    Reduced Kernel Extreme Learning Machine for Traffic Sign Recognition

    Sanz-Madoz, E. / Echanobe, J. / Mata-Carballeira, O. et al. | IEEE | 2019


    Traffic Sign Recognition Algorithm Based on Improved YOLOv5

    Sang, Zhengxiao / Xia, Fuming / Huang, Han et al. | IEEE | 2022


    Traffic sign recognition algorithm based on improved ResNet18

    Hu, Yixin / Ye, Qingyang / Zhu, Xuanqi et al. | SPIE | 2024


    TRAFFIC SIGN RECOGNITION DEVICE AND TRAFFIC SIGN RECOGNITION METHOD

    MIYASATO KAZUHIRO / KOYASU TOSHIYA | Europäisches Patentamt | 2023

    Freier Zugriff