Traffic accidents caused by distracted drivers account for a large proportion of traffic accidents each year, and monitoring the driving state of drivers to avoid traffic accidents caused by distracted driving has become a very important research direction. At present, the field of driver distraction detection mainly adopts supervised learning methods, which have problems such as poor generalization ability, large labeling cost, and weak artificial intelligence. This paper is oriented toward driver distraction fine-grained detection and innovatively proposes a new unsupervised deep learning algorithm, which is referred to as UDL, to achieve a more human-like level of intelligence. First, we build a new unsupervised deep learning algorithm; furthermore, we integrate the multilayer perceptron (MLP) architecture to build a new backbone and projection head to strengthen feature extraction capabilities; and finally, a new loss function based on contrast learning and a stop-gradient strategy is designed to guide the model to learn more robust features. The comparison results on large-scale driver distraction detection datasets show that our UDL method can accurately detect driver distraction without labels and exhibits excellent generalization performance with a linear evaluation accuracy of 97.38%; In addition, after fine-tuning with fewer labels, our UDL method can achieve superior performance close to state-of-the-art supervised learning methods, achieving 99.07% accuracy after fine-tuning using only 50% of the labeled data, which greatly reduces the cost and limitations of manual annotation.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A New Unsupervised Deep Learning Algorithm for Fine-Grained Detection of Driver Distraction


    Beteiligte:
    Li, Bing (Autor:in) / Chen, Jie (Autor:in) / Huang, Zhixiang (Autor:in) / Wang, Haitao (Autor:in) / Lv, Jianming (Autor:in) / Xi, Jingmin (Autor:in) / Zhang, Jun (Autor:in) / Wu, Zhongcheng (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    01.10.2022


    Format / Umfang :

    3232234 byte




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    METHOD FOR FINE-GRAINED DETECTION OF DRIVER DISTRACTION BASED ON UNSUPERVISED LEARNING

    CHEN JIE / LI BING / CHENG ZIHAN et al. | Europäisches Patentamt | 2023

    Freier Zugriff

    Driver distraction fine-grained monitoring method and system

    CHEN JIE / JIANG YANAN / ZHAO CHEN et al. | Europäisches Patentamt | 2020

    Freier Zugriff

    Driver Visual Distraction Detection Using Unsupervised Learning Techniques

    Hamieh, Salam / Heiries, Vincent / Al-Osman, Hussein et al. | IEEE | 2023


    Fine-Grained Detection of Driver Distraction Based on Neural Architecture Search

    Chen, Jie / Jiang, YaNan / Huang, ZhiXiang et al. | IEEE | 2021