现代制造工程Issue(12)：120-129,10.DOI:10.16731/j.cnki.1671-3133.2024.12.016
基于改进YOLOv8n的铝型材表面小目标缺陷检测方法
A small target defect detection method for aluminum profile surface based on improved YOLOv8n
摘要
Abstract
To address the issues of slow surface defect detection speed in industrial aluminum profiles,as well as low accuracy in detecting small target defects such as scratches and paint bubbles,a small target aluminum profile surface defect detection model based on improved YOLOv8n was proposed.Firstly,cross-scale feature fusion was employed to enhance the network's ability to perceive multiscale information,by integrating features from different scales.Secondly,the C2f structure of the YOLOv8 backbone network was improved by incorporating RFAConv attention convolution.This was done to design a new structure to enhance the network's feature extraction ability and improve the accuracy of small object detection.In the neck region,lightweight RepGhost modules were used to reduce parameter count and computational complexity.Finally,the original YOLOv8's CIoU loss function was replaced with WIoUV3 to improve detection accuracy and convergence speed.Extensive experiments on aluminum profile de-fect datasets showed the model's effectiveness.It achieved a 2.5％increase in mAP@0.5,The frame rate has reached 110 fps,and reduced weight file size by 16.2％,surpassing the original model's performance.Experiments on the VOC2012 dataset and the Northeastern University hot rolled strip surface defect dataset demonstrated the algorithm's robustness.The refined model ef-fectively meets industrial demands for aluminum profile detection.关键词
表面缺陷检测/注意力卷积/特征融合/YOLOv8/小目标检测Key words
surface defect detection/attention convolution/feature fusion/YOLOv8/small object detection分类
信息技术与安全科学引用本文复制引用
孙铁强,刘俊,宋超,肖鹏程..基于改进YOLOv8n的铝型材表面小目标缺陷检测方法[J].现代制造工程,2024,(12):120-129,10.基金项目
河北省"三三三人才工程"资助项目(A202102002) （A202102002）
河北省创新能力提升计划项目(23561007D) （23561007D）
2023年唐山市重点研发项目(23140204A) （23140204A）
