In recent years, the number of cars in the world continues to increase, but more and more road vehicle driving safety risks are also growing, so the demand for the improvement of safe driving assistant driving system is getting stronger and stronger. The purpose of this paper is to study the vehicle road condition perception system based on machine vision and intelligent perception and to provide technical suggestions for it. The system can analyze and process the images collected by monocular camera and obtain the position information of lane lines and vehicles, which can greatly improve the safety of driving on the road. Firstly, the concept of machine vision and image preprocessing before two key technologies is described in this paper. Secondly, the vehicle detection method is further studied, and a set of better methods to solve the requirements of high accuracy and real-time performance are sorted out according to the requirements of accuracy and real-time performance of vehicle detection. The detection process was divided into hypothesis generation module and hypothesis verification module. The results showed that the PSNR increased by 1 dB under the optimized image recognition technology. Based on this, technical optimization is carried out for the accuracy of the current vehicle road condition perception system.


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    Titel :

    Vehicle Road Condition Perception System Based on Machine Vision and Intelligent Perception


    Weitere Titelangaben:

    Lect. Notes Electrical Eng.


    Beteiligte:
    Chang, Jia-Wei (Herausgeber:in) / Yen, Neil (Herausgeber:in) / Hung, Jason C. (Herausgeber:in) / Yao, Jin (Autor:in) / Feng, Jing (Autor:in) / Liu, Yuzhou (Autor:in) / Chen, Licheng (Autor:in) / You, Rentang (Autor:in) / Sun, Jiaxing (Autor:in) / Zhang, Xiaofei (Autor:in)

    Kongress:

    International Conference on Frontier Computing ; 2020 ; Singapore, Singapore July 10, 2020 - July 13, 2020


    Erschienen in:

    Frontier Computing ; Kapitel : 51 ; 467-474


    Erscheinungsdatum :

    01.01.2022


    Format / Umfang :

    8 pages





    Medientyp :

    Aufsatz/Kapitel (Buch)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




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