广西师范大学学报（自然科学版）2025，Vol.43Issue(3)：72-83,12.DOI:10.16088/j.issn.1001-6600.2024120101
基于YOLOv8的雾天车辆行人实时检测方法
A YOLOv8-based Real-time Object Detection Method for Vehicles and Pedestrians in Foggy Weather
摘要
Abstract
With the extensive application of intelligent communication technology in smart traffic scenarios,the task of detecting pedestrians and vehicles constitutes an important technical means for road safety.In light of the high missed detection rate and slow detection speed in the foggy environment,a real-time foggy target detection method based on YOLOv8 is proposed.The model incorporates the fog removal network module into the input image to preprocess it,retains the detailed features of the original image and eliminates the obstruction of fog,and then utilizes the improved YOLOv8n for detection.On YOLOv8n,the C2f module is enhanced based on FasterNet to reduce the model parameters and size,increase the model's computing efficiency,and the SE-ResNeXt detection head is designed to avoid the negative impacts of stacking neural network layers.Finally,knowledge distillation is employed to further enhance the detection accuracy.The proposed model is validated on the RTTS dataset and the synthetic foggy dataset.Compared with the original network,the average precision(mAP@50_95)is improved by 5.2 percentage points,and the detection frame rate reaches 170 frame/s.关键词
雾天场景/目标检测/信息交互/FasterNet/SENet/ResNeXtKey words
foggy scene/object detection/information interactive/Fasternet/SE-Net/ResNeXt分类
信息技术与安全科学引用本文复制引用
汤亮,陈博文,牛一森,马荣庚..基于YOLOv8的雾天车辆行人实时检测方法[J].广西师范大学学报（自然科学版）,2025,43(3):72-83,12.基金项目
国家科技支撑计划项目(2016IM020200-01) （2016IM020200-01）
