Object detection for traffic management based on YOLO

Xuebin Hong; Jubin Huang; Weiwei Zhao; Huiwen Zou; Zhe Lin; Yuecheng Chen

doi:10.1117/12.3024069

14 February 2024 Object detection for traffic management based on YOLO

Xuebin Hong, Jubin Huang, Weiwei Zhao, Huiwen Zou, Zhe Lin, Yuecheng Chen

Proceedings Volume 13018, International Conference on Smart Transportation and City Engineering (STCE 2023); 130180M (2024) https://doi.org/10.1117/12.3024069
Event: International Conference on Smart Transportation and City Engineering (STCE 2023), 2023, Chongqing, China

Abstract

This paper presents an object detection method for traffic management based on the YOLOV7 model, using the bdd100k dataset for experimentation. The results show that the proposed method has good detection performance in traffic scenes. The main contribution of this paper is to improve the accuracy and efficiency of object detection in traffic scenes by applying the YOLOV7 model to the field of traffic management. The research results of this paper are of great significance for the optimization and improvement of traffic management systems. Future research can explore YOLOV7's performance on other target categories and consider algorithm optimizations to improve accuracy on new datasets.

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

Citation Download Citation

Xuebin Hong, Jubin Huang, Weiwei Zhao, Huiwen Zou, Zhe Lin, and Yuecheng Chen "Object detection for traffic management based on YOLO", Proc. SPIE 13018, International Conference on Smart Transportation and City Engineering (STCE 2023), 130180M (14 February 2024); https://doi.org/10.1117/12.3024069

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