计算机工程2025，Vol.51Issue(5)：133-142,10.DOI:10.19678/j.issn.1000-3428.0069026
基于改进YOLOv8的密集行人检测模型
Aggregation Pedestrian Detection Model Based on Improved YOLOv8
摘要
Abstract
Pedestrian detection in crowded scenes is a key technology in intelligent monitoring of public space.It enables the intelligent monitoring of crowds,using object detection methods to detect the positions and number of pedestrians in videos.This paper presents Crowd-YOLOv8,an improved version of the YOLOv8 detection model,to address the issue of pedestrians being easily missed owing to occlusion and small target size in densely populated areas.First,nostride-Conv-SPD is introduced into the backbone network to enhance its capability of extracting fine-grained information,such as small object features in images.Second,small object detection heads and the CARAFE upsampling operator are introduced into the neck part of the YOLOv8 network to fuse features at different scales and improve the detection performance in the case of small targets.Experimental results demonstrate that the proposed method achieves an mAP@0.5 of 84.3％and an mAP@0.5∶0.95 of 58.2％on a CrowdedHuman dataset,which is an improvement of 3.7 and 5.2 percentage points,respectively,compared to those of the original YOLOv8n.On the WiderPerson dataset,the proposed method achieves an mAP@0.5 of 88.4％and an mAP@0.5∶0.95 of 67.4％,which is an improvement of 1.1 and 1.5 percentage points compared to those of the original YOLOv8n.关键词
密集行人检测/YOLOv8网络/nostride-Conv-SPD模块/CARAFE算子/小目标检测头Key words
aggregation pedestrian detection/YOLOv8 network/nostride-Conv-SPD module/CARAFE operator/small object detection head分类
计算机与自动化引用本文复制引用
黄昆,齐肇建,王娟敏,胡倩,胡伟超,皮建勇..基于改进YOLOv8的密集行人检测模型[J].计算机工程,2025,51(5):133-142,10.基金项目
贵州省科技支撑计划(黔科合支撑[2023]一般430). （黔科合支撑[2023]一般430）
