郑州大学学报（工学版）2025，Vol.46Issue(4)：40-46,7.DOI:10.13705/j.issn.1671-6833.2025.04.010
基于改进YOLOv8的遗留物品检测算法
Abandoned Object Detection Algorithm Based on Improved YOLOv8
摘要
Abstract
An abandoned object detection algorithm based on improved YOLOv8 was proposed to address the diffi-culties of traditional background subtraction based abandoned object detection algorithms in dealing with crowded environments,small targets,occlusion,and light changes,as well as the low accuracy of models based on deep learning methods.Firstly,dynamic upsampling DySample was used to replace the nearest neighbor upsampling,op-timizing the upsampling process,and increasing the model's generalization ability.Secondly,the downsampling convolution was replaced with the efficient lightweight ADown module which reduced the overall model parameters while improving the detection accuracy of the algorithm.In addition,the introduction of EMA attention mechanism optimized the feature extraction process,enhanced feature extraction capabilities,and improved the effectiveness of small object detection.The experimental results showed that the improved model YOLO-DAE achieved P,R,and mAP@50 and mAP@50:95 was 93.4%,87.7%,91.7%,and 80.2%,respectively,which was 1.8,1.6,1.2,and 2.1 percentage points higher than the original YOLOv8s.And the average accuracy mAP@50 and mAP@50:95 was higher than YOLOv5s r6.0,YOLOv6s v3.0,YOLOv7s AF,and YOLOv9s,effectively improving the abili-ty to detect abandoned object.关键词
遗留物品检测/YOLOv8算法/EMA注意力机制/DySample模块/ADown模块Key words
abandoned object detection/YOLOv8 algorithm/EMA attention mechanisms/DySample module/A-Down module分类
信息技术与安全科学引用本文复制引用
张震,葛帅兵,陈可鑫,李友好,黄伟涛..基于改进YOLOv8的遗留物品检测算法[J].郑州大学学报（工学版）,2025,46(4):40-46,7.基金项目
河南省重点研发专项(231111211600) （231111211600）
河南省重大公益专项(201300311200) （201300311200）
