优化实时交通流检测:一种将YOLO与图像预处理结合的研究OA

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.