计算机工程与应用2026，Vol.62Issue(6)：122-133,12.DOI:10.3778/j.issn.1002-8331.2507-0218
YOLO-PD:轻量级实时行人检测算法
YOLO-PD:Lightweight Real-Time Pedestrian Detection Algorithm
摘要
Abstract
To address the limited resources of edge devices and the low detection accuracy of pedestrian detection algo-rithms on small objects,multi-scale objects,and geometrically deformed objects caused by overlapping occlusions,YOLO-PD is proposed which is a YOLOv8-based pedestrian detection algorithm suitable for edge deployment.For small objects,a partial Transformer block(PTB)is designed to enhance feature extraction.Hybrid structure of PTB maintains efficient feature extraction while reducing computational costs.For multi-scale objects,a pyramid shared dilation convolu-tion(PSDC)is introduced,leveraging shared-weight multi-scale dilation convolutions to improve multi-scale feature extraction while minimizing module size.For geometrically deformed objects,a light deformable dynamic head(LDDH)is developed.It dynamically adjusts weighting factors to improve detection accuracy and employs deformable convolu-tions to better capture features of deformed objects.Experimental results show that compared to the baseline model YOLOv8n,YOLO-PD achieves 2.9 and 1.9 percentage points higher mAP50 on the custom pedestrian detection datasets COCO-Person and VOC-Person,respectively,while reducing parameters by 34.3%.On the public dataset WidePerson,it improves mAP50 by 1.8 percentage points and mAP50:95 by 1.1 percentage points.The algorithm excels in pedestrian detection with high accuracy,strong generalization,and minimal parameters,making it ideal for edge devices.关键词
YOLOv8/行人检测/WidePerson数据集Key words
YOLOv8/pedestrian detection/WidePerson dataset分类
信息技术与安全科学引用本文复制引用
陈胜宝,施隆照..YOLO-PD:轻量级实时行人检测算法[J].计算机工程与应用,2026,62(6):122-133,12.基金项目
福建省自然科学基金(2022J02015). （2022J02015）
