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

作　　者：解妙霞[1] 程照中 李嘉乐 李玲[1] 贺宁 XIE Miaoxia;CHENG Zhaozhong;LI Jiale;LI Ling;HE Ning(School of Electromechanical Engineering,Xi’an University of Architecture and Technology,Xi’an 710055,China;School of Information and Control,Xi’an University of Architecture and Technology,Xi’an 710055,China)

机构地区：[1]西安建筑科技大学机电工程学院,陕西西安710055 [2]西安建筑科技大学信息与控制学院,陕西西安710055

出　　处：《湖南大学学报(自然科学版)》2025年第4期135-148,共14页Journal of Hunan University:Natural Sciences

基　　金：陕西省重点研发计划项目(2024GX-YBXM-178,2022NY—094)。

摘　　要：为了提高钢材缺陷检测精度,提出一种基于YOLOv5s的缺陷检测算法YOLOv5sFNCE.首先,在骨干特征提取网络中加入新型NAMAttention注意力机制,提高对目标的感知和区分能力;并提出新型的C3-Faster,通过减小内存访问和冗余计算更有效地提取特征;在特征融合网络和输出端引入位置卷积CoordConvs,增强目标的语义感知能力和全局感知能力;最后,引入新的损失函数Focal-EIoU,以加快收敛速度,提高回归精度.实验结果表明,YOLOv5sFNCE算法在钢材表面缺陷数据集上的均值平均精度达到了75.1%,比原始YOLOv5s提高了1.7个百分点,检测速度则提升了20.5%,证明了该算法在钢材缺陷检测中能够有效提升检测速度和检测精度.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 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TG146[自动化与计算机技术—计算机科学与技术]