Research on steel surface detection based on YOLOv5s-SNTC model
简介：Steel surface detection is a critical component in ensuring product quality.Traditional manual and photoelectric inspection methods suffer from low efficiency,high costs and high false detection rates.To address these issues,this paper presents a lightweight,high-precision steel surface detec-tion model YOLOv5s(you only look once version 5 small)-SNTC(short for Slim-Neck with three CBAM modules).To start with,the CBAM(short for convolutional block attention module)module is integrated into the backbone to enhance feature representation capability.Subsequently,the neck is replaced with a Slim-Neck structure to improve feature fusion performance while significantly re-ducing the model's parameter count and computational complexity.Furthermore,the model's net-work structure is meticulously designed with computational resources allocated optimally across layers to ensure the model sustaining lightweight properties while fully exerting its detection performance.Finally,extensive experiments conducted on the NEU-DET(NEU surface defect database)dataset validate that the YOLOv5s-SNTC model achieves a mean average precision(mAP)of 76.6%and a detection speed of 155 frames per second.Compared to the baseline model YOLOv5s,the mAP of the YOLOv5s-SNTC model is significantly improved by 7.6%.Meanwhile,its model size is com-pressed to 11.6 MB,and its computational load is reduced to 13.9 giga floating-point operations per second(GFLOPS).This demonstrates that the model proposed here can effectively improve the detection speed and accuracy in steel surface detection without significantly increasing complex-ity and resource consumption,achieving an optimal balance between lightweight design and high precision.展开
学者：PENGSiyuLIDingxinWangHuanFangTianDongpingShiZhongzhi
关键词：Yolov5ssteel surfaceobject detectionconvolutional block attention module
在线出版日期：2026-04-17 （网站首发日期）
