Parking lot vehicle recognition system

Shenghua He; Li Xie; Yuanzhou Huang; Haoxiang Wang; Xiaoyu Sun

doi:10.1117/12.3037817

12 September 2024 Parking lot vehicle recognition system

Shenghua He, Li Xie, Yuanzhou Huang, Haoxiang Wang, Xiaoyu Sun

Author Affiliations +

Proceedings Volume 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024); 1325617 (2024) https://doi.org/10.1117/12.3037817
Event: Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 2024, Anshan, China

Abstract

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.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Shenghua He, Li Xie, Yuanzhou Huang, Haoxiang Wang, and Xiaoyu Sun "Parking lot vehicle recognition system", Proc. SPIE 13256, Fourth International Conference on Computer Vision and Pattern Analysis (ICCPA 2024), 1325617 (12 September 2024); https://doi.org/10.1117/12.3037817

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

;

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE