改进YOLOv8n的尘雾环境下目标检测算法

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主题词：自动驾驶目标检测注意力机制多尺度特征融合尘雾环境中图分类号：U469.6；TP242.6 文献标志码：A DOI： 10.19620/j.cnki.1000-3703.20240036

ImprovedYOLOv8n ObjectDetection AlgorithminDust andFogEnvironment

WangZiyu’，ZhangJiancheng²，LiuYuansheng² （1.Scholof UrbanRail TransitandLogistics，Beijing Union University，Beijing1Oo101;2.Schoolof Robotics， Beijing Union University，Beijing100101）

【Abstract】To address the issues of missed detections，false detections and lowaccuracy in detecting smalland distant objects underadverse conditions such as dustand haze，this paper proposestheEPM-YOLOv8object detection algorithm.The Eficient ChannelAtention （ECA）moduleisintegratedintotheC2f moduleof theYOLOv8nalgorithm，enablingthebackbone network to focus more effectivelyonshallowandsmallrobjectfeatures.Byadding anadditional detectionlayeranddesigning a multi-dimension feature fusion architecture，the model'sability to extracttarget featuresandits detectionaccuracyare significantlyimproved.Furthermore，alossfunctionbasedontheMinimumPointDistance IntersectionoverUnion（MPDIoU） is employedtoenhance theprecisionofboundingboxregresion.ExperimentalresultsdemonstratethattheEPM-YOLOv8model achieves a precision ratio of 83.6% and a detection accuracy of 76.8% ，exhibiting superior detection performance under challenging conditions such as haze and dust.

Key Words：Autonomous driving，Object detection，Attention mechanism，Multi-scale feature fusion，Dusty and foggy environment

【引用格式】王子钰，张建成，刘元盛.改进YOLOv8n的尘雾环境下目标检测算法[J].汽车技术，2025（6）：1-7. WANGZY，ZHANGJC，LIUYS.ImprovedYOLOv8n Object DetectionAlgorithminDustandFog Environment[J]. Automobile Technology，2025（6）： 1-7.

1前言

在尘雾环境中，图像模糊、质量下降导致有效特征提取困难，目标检测任务易出现精度降低、错检和漏检等问题。（剩余8894字）