Steel Surface Defect Detection Algorithm Based on Improved YOLOv8n
YAO Ruoyu
ZHENG Shiling
SHI Yixuan
ZHANG Siqi
ZHANG Xinfei
GAO Feilong
ZHANG Xia
Abstract：Aiming at the current problem of low detection accuracy due to the complexity of target features in steel defect detection,a steel defect detection algorithm,GOS-YOLO,based on improved YOLOv8n is proposed.Firstly,a Slim-neck paradigm constructed by lightweight-convolution GSConv is used as the feature fusion network,which improves the accuracy while reducing the number of model parameters.Secondly,some of the C2f modules of the backbone network are replaced with C2f_ODConv modules com-bined with full-dimensional dynamic convolution(ODConv),to achieve multi-dimensional feature atten-tion of the model and thus improve the accuracy of the model.Finally,the SENetV2 attention mecha-nism,which combines a multi-branch structure with squeeze and excitation operation,is embedded into the neck network to enhance the model's ability to extract complex features.The experimental results show that on the NEU-DET dataset,the R,mAP50,and mAP50～95 of GOS-YOLO are improved by 3.3％,1.7％,and 2.3％,respectively,compared with YOLOv8n.On the VOC2007 dataset mAP50～95 improved by 1％and FLOPs decreased by 16％.
Keywords：deep learningcomputer visionYOLOv8defect detection
Publication Date：2025-04-27
Online Publishing Date：2026-08-28(First online date of this platform, not the publication date of the document)
Pages：13( 177-189 )