Intelligent driving technology is an important direction of the current development of automobile technology. The realization of intelligent technology is based on the perception ability of car sensors such as cameras and lidar for the driving environment detection. In the actual driving, the surface pollution of sensors will reduce the safety of intelligent driving vehicles. Therefore, it is very important to realize the automatic recognition of ultra-near-field dirt on the optical surface of car sensors. At present, the research on camera sensor fouling recognition is very scarce, and the relevant public data set for recognition research is also lacking. This paper conducted an in-depth study on the contamination problem of the camera sensor of the autonomous vehicle, independently created a dataset of dirt on the camera sensor, and trained the dataset based on the YOLO-V5 model using this dataset. The results show that the dataset we constructed is quite reasonable, with obvious features and excellent training results, with mAP(0.5) reaching 0.987; The results of the detection experiment prove that the trained dirt recognition model has certain universality, and has relatively excellent detection effect on different datasets, and the accuracy rate and recall rate can reach more than 0.8. This work could provide a certain reference for the cleaning research of automatic driving camera sensor.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Research on sensor dirt recognition of autonomous vehicle camera based on deep learning


    Beteiligte:
    Li, Yonghua (Herausgeber:in) / Yao, Hanbing (Herausgeber:in) / Liu, Xing (Herausgeber:in) / Wu, Zhijun (Autor:in) / Wang, Yuchang (Autor:in) / Ran, Lujia (Autor:in) / Hu, Zongjie (Autor:in) / Liu, Zhuolin (Autor:in) / Ding, Xiangrui (Autor:in) / Chen, Zhengzhe (Autor:in)

    Kongress:

    Second International Conference on Informatics, Networking, and Computing (ICINC 2023) ; 2023 ; Wuhan, China


    Erschienen in:

    Proc. SPIE ; 13078


    Erscheinungsdatum :

    03.04.2024





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    ENVIRONMENT RECOGNITION SYSTEM, VEHICLE, AND CAMERA DIRT DETECTION METHOD

    AKIYAMA YASUHIRO / NAKAMURA KATSUYUKI / IRIE KOTA | Europäisches Patentamt | 2015

    Freier Zugriff

    VEHICLE CAMERA LENS DIRT PROTECTION VIA AIR FLOW

    BOEGEL RUEDIGER / KORAVADI KRISHNA | Europäisches Patentamt | 2017

    Freier Zugriff

    Vehicle camera lens dirt protection via air flow

    BOEGEL RUEDIGER / KORAVADI KRISHNA | Europäisches Patentamt | 2017

    Freier Zugriff

    Vehicle camera lens dirt protection via air flow

    BOEGEL RUEDIGER / KORAVADI KRISHNA | Europäisches Patentamt | 2022

    Freier Zugriff

    Vehicle-mounted camera dirt removal method and device based on ultrasonic waves

    DENG BO / WANG YAJUN / MASUDA SATORU et al. | Europäisches Patentamt | 2023

    Freier Zugriff