In modern cities, as the demand for transportation increases, the number of vehicles continues to rise, thereby increasing the demand for multi-level parking garages. Unlike traditional open-air parking lots, multi-level parking garages need to consider the weight of different vehicles and require classification management of vehicles. Traditional weighing sensors typically rely on large-scale equipment and complex construction, resulting in higher costs.This paper takes a purely visual approach and utilizes ResNet18 to construct a vehicle classification method that divides vehicles into six categories. This method serves as an auxiliary system for multi-level parking garages, achieving purely visual processing of tasks that previously required multiple sensors. In the final training evaluation, our approach demonstrates 97.3% accuracy, 95.5% precision, 95% recall, and 96.3% F1 score. Additionally, we use TensorRT to accelerate the inference process, keeping the inference time within 2 milliseconds, enabling rapid inference that can be completed without parking.


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    Title :

    Parking lot vehicle recognition system


    Contributors:
    Zhao, Ji (editor) / Yang, Yonghui (editor) / He, Shenghua (author) / Xie, Li (author) / Huang, Yuanzhou (author) / Wang, Haoxiang (author) / Sun, Xiaoyu (author)

    Conference:

    Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024) ; 2024 ; Anshan, China


    Published in:

    Proc. SPIE ; 13256 ; 1325617


    Publication date :

    2024-09-12





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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