基于YOLOv7-tiny的轻量级密集人群场景行人检测算法  

Lightweight Pedestrian Detection Algorithm for Dense Crowd Scenes based on YOLOv7

作　　者：张芯源 高志刚 冯建文[1] ZHANG Xinyuan;GAO Zhigang;FENG Jianwen(School of Computer,Hangzhou Dianzi University,Hangzhou 310018,China;College of Information Engineering,China Jiliang University,Hangzhou 310018,China)

机构地区：[1]杭州电子科技大学计算机学院,浙江杭州310018 [2]中国计量大学信息工程学院,浙江杭州310018

出　　处：《软件工程》2025年第1期46-51,共6页Software Engineering

摘　　要：针对现有的高精度行人检测模型因资源要求高而导致的难以应用于边缘计算场景的问题,提出了一种适用于边缘GPU设备的轻量级实时密集行人检测算法。该算法通过在检测头中融合全维度动态卷积,降低了冗余信息对于检测效果的影响,并通过优化损失函数增强了算法区分待检测目标和背景的能力。实验结果表明,在密集人群场景下的行人检测任务中,该算法在精确度方面较本文基线算法YOLOv7-tiny提升了4.1百分点,这证明该算法能够在边缘计算场景下实现准确的密集人群检测。In response to the challenges posed by existing high-precision pedestrian detection models,which require substantial resources and are thus difficult to apply in edge computing scenarios,this paper proposes a lightweight real-time dense pedestrian detection algorithm suitable for edge GPU devices.This algorithm reduces the impact of redundant information on detection performance by integrating full-dimensional dynamic convolution in the detection head,and enhances the algorithm's ability to distinguish between the target to be detected and the background through optimizing the loss function.Experimental results demonstrate that in pedestrian detection tasks within densely populated scenes,this algorithm improves accuracy by 4.1 percentage points compared to the baseline algorithm YOLOv7-tiny presented in this paper,proving that it can achieve accurate dense crowd detection in edge computing scenarios.

关 键 词：行人检测 小目标识别 深度学习 边缘计算 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]