湖南大学学报（自然科学版）2025，Vol.52Issue(4)：135-148,14.DOI:10.16339/j.cnki.hdxbzkb.2025273
基于位置感应卷积与注意力机制的钢材缺陷检测
Steel Defect Detection Based on Position-sensitive Convolution and Attention Mechanisms
摘要
Abstract
To improve the accuracy of steel defect detection,a defect detection algorithm YOLOv5s-FNCE based on YOLOv5s is proposed.Firstly,a novel NAMAttention attention mechanism is added to the backbone feature extraction network to improve the perception and differentiation of the target;and a new C3-Faster is proposed to extract the features;the positional convolutional CoordConvs is introduced in the feature fusion network and at the output to enhance the semantic perception ability and global perception ability of the target;and finally,a new loss function Focal-EIoU is introduced to accelerate the convergence speed and improve the regression accuracy.Experimental results show that the mean average accuracy of the YOLOv5s-FNCE algorithm on the steel surface defects dataset reaches 75.1%,which is 1.7%higher than that of the original YOLOv5s,the detection speed is increased by 20.5%,which proves that the algorithm can effectively improve the detection speed and accuracy in steel defect detection.关键词
目标检测/YOLOv5/位置感应/损失函数/注意力机制/钢材缺陷Key words
target detection/YOLOv5/position sensor/loss function/attention mechanism/steel defect分类
信息技术与安全科学引用本文复制引用
解妙霞,程照中,李嘉乐,李玲,贺宁..基于位置感应卷积与注意力机制的钢材缺陷检测[J].湖南大学学报（自然科学版）,2025,52(4):135-148,14.基金项目
陕西省重点研发计划项目(2024GX-YBXM-178,2022NY—094),Key R&D Program Projects of Shaanxi Province(2024GX-YBXM-178,2022NY—094) （2024GX-YBXM-178,2022NY—094）
