计算机工程2025，Vol.51Issue(5)：326-339,14.DOI:10.19678/j.issn.1000-3428.0069259
基于改进YOLOv8n的手机屏幕瑕疵检测算法:PGS-YOLO
Mobile Phone Screen Defect Detection Algorithm Based on Improved YOLOv8n:PGS-YOLO
摘要
Abstract
As the main window of human-computer interaction,the mobile phone screen has become an important factor affecting the user experience and the overall performance of the terminal.As a result,there is a growing demand to address defects in mobile phone screens.To meet this demand,in view of the low detection accuracy,high missed detection rate of small target defects,and slow detection speed in the process of defect detection on mobile phone screens,a PGS-YOLO algorithm is proposed,with YOLOv8n as the benchmark model.PGS-YOLO effectively improves the detection ability of small targets by adding a special small target detection head and combining it with the SeaAttention attention module.The backbone and feature fusion networks are integrated into PConv and GhostNetV2 lightweight modules,respectively,to ensure accuracy,reduce the number of model parameters,and improve the speed and efficiency of defect detection.The experimental results show that,in the dataset of mobile phone screen surface defects from Peking University,compared with the results of YOLOv8n,the mAP@0.5 and mAP@0.5∶0.95 of the PGS-YOLO algorithm are increased by 2.5 and 2.2 percentage points,respectively.The algorithm can accurately detect large defects in the process of mobile phone screen defect detection as well as maintain a certain degree of accuracy for small defects.In addition,the detection performance is better than that of most YOLO series algorithms,such as YOLOv5n and YOLOv8s.Simultaneously,the number of parameters is only 2.0×106,which is smaller than that of YOLOv8n,meeting the needs of industrial scenarios for mobile phone screen defect detection.关键词
YOLOv8n模型/手机屏幕瑕疵检测/小目标检测/部分卷积/GhostNetV2轻量化模块/挤压增强轴向注意力Key words
YOLOv8n model/mobile phone screen defect detection/small target detection/partial convolution/GhostNetV2 lightweight module/squeeze enhances axial attention分类
信息技术与安全科学引用本文复制引用
周思瑜,徐慧英,朱信忠,黄晓,盛轲,曹雨淇,陈晨..基于改进YOLOv8n的手机屏幕瑕疵检测算法:PGS-YOLO[J].计算机工程,2025,51(5):326-339,14.基金项目
国家自然科学基金(61976196) （61976196）
浙江省自然科学基金重点项目(LZ22F030003) （LZ22F030003）
国家级大学生创新创业训练计划项目创新训练重点项目(202310345042). （202310345042）
