In recent years, the rapid growth of Internet of Things (IoT) technology and computer vision (CV) technology has provided strong support for the application of deep learning (DL) in various fields. Among them, DL based object detection algorithms have attracted much attention due to their excellent performance. Vehicle target detection, as an important branch of object detection, is crucial for the construction of intelligent driving and intelligent transportation systems. In autonomous driving technology, using machines to detect and recognize traffic signs, vehicles, and pedestrians is of great significance for improving driving safety, reducing labor costs, and demonstrating broad application prospects. In this paper, a target recognition algorithm based on CV and DL is proposed to improve the target detection capability of auto drive system. This algorithm combines the image processing technology of CV and the feature extraction and classification ability of DL to achieve fast and accurate recognition of targets such as vehicles and pedestrians in road environments. Experimental results show that through training and optimizing the DL model, the algorithm can perform well in complex and changeable traffic scenes and provide reliable target detection support for the auto drive system.


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    Title :

    The Application of Computer Vision Target Recognition Technology in Autonomous Driving


    Contributors:
    Wang, Yaning (author) / Bi, Xin (author)


    Publication date :

    2024-07-29


    Size :

    422203 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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