聊城大学学报（自然科学版）2026，Vol.39Issue(2)：238-248,11.DOI:10.19728/j.issn1672-6634.2025050002
优化实时交通流检测:一种将YOLO与图像预处理结合的研究
Optimizing real-time traffic flow detection:an integrated approach combining YOLO with advanced image preprocessing
摘要
Abstract
To improve the efficiency of urban traffic management,the application of intelligent transporta-tion systems(ITS)has become a practical and effective approach.Real-time traffic flow monitoring tech-nology provides essential data support for such systems.This study focuses on enhancing the performance of terminal devices used for real-time traffic monitoring and proposes a method based on the YOLO object detection algorithm.By incorporating image preprocessing techniques,the system achieves improved de-tection accuracy while reducing computational resource consumption at the terminal.The research utilizes a subset of the COCO public dataset to construct a customized training dataset suitable for this task.YOLOv5s and YOLOv8s models were trained and comprehensively evaluated across various scenarios,in-cluding dynamic video and real-time video streams.Techniques such as background subtraction,Contrast Limited Adaptive Histogram Equalization(CLAHE),and median filtering were applied to enhance input image quality.Experimental results demonstrate that these preprocessing methods improve detection accu-racy by approximately 1.2%to 1.8%under different testing conditions and environmental complexities,while also reducing resource usage.This study systematically analyzes key components including model training,image processing,and performance evaluation.Through a series of video-based and real-world simulation experiments,the proposed approach is shown to have practical value for intelligent traffic appli-cations.关键词
智能交通/YOLO/车流量实时监测/模型训练/图像预处理Key words
intelligent transportation/YOLO/real-time vehicle flow monitoring/model training/image preprocessing分类
数理科学引用本文复制引用
孙嘉豪,邹瑞滨,李和福,高扬,张葳琳,刘沅鑫..优化实时交通流检测:一种将YOLO与图像预处理结合的研究[J].聊城大学学报（自然科学版）,2026,39(2):238-248,11.基金项目
山东省自然科学基金项目(ZR2021MF097)资助 （ZR2021MF097）
