计算机与现代化Issue(10)：24-31,8.DOI:10.3969/j.issn.1006-2475.2025.10.005
改进YOLOv8的密集行人检测算法
Dense Pedestrian Detection Algorithm Based on Improved YOLOv8
摘要
Abstract
To address the issues of missed and false detections in dense pedestrian scenarios caused by complex backgrounds,high crowd density,low-light conditions,and partial occlusions,this paper proposes an optimized dense pedestrian detection al-gorithm based on YOLOv8n.The algorithm replaces the original convolutional blocks in the backbone network with efficient GSConv convolutions,reducing the model's computational load while maintaining recognition accuracy.Additionally,GSConv convolutions enable the model to run efficiently on standard GPUs.The feature fusion network is replaced with the SlimNeck lightweight feature fusion module,which reduces the number of feature channels,thereby improving the model's detection preci-sion and speed.An EMA attention mechanism is embedded in the feature extraction network to enhance the model's ability to capture both global and local information,thereby reducing false and missed detections in dense pedestrian scenarios.The algo-rithm also incorporates the Repulsion Loss function to better handle overlaps and occlusions among adjacent pedestrians in dense pedestrian detection,reducing interference between targets and optimizing bounding box regression.Training and validation on the CrowdHuman dataset demonstrate that the improved YOLOv8 model yields a 4.5 percentage points increase in mAP over the baseline.Furthermore,the model exhibits superior performance in dense crowds,occlusions,small-object detection,and low-light conditions,thereby offering an efficient and robust solution for dense pedestrian detection.关键词
YOLOv8/复杂场景/密集行人识别/Repulsion损失函数/目标检测/Slim-NeckKey words
YOLOv8/complex scenarios/dense crowd recognition/repulsion loss function/object detection/Slim-Neck分类
计算机与自动化引用本文复制引用
段警韦,陈亮,李雪,刘蒙蒙,刘晋宇..改进YOLOv8的密集行人检测算法[J].计算机与现代化,2025,(10):24-31,8.基金项目
陕西省教育厅重点科学研究计划项目(22JS021) （22JS021）
