基于改进YOLOv7-tiny的铝型材表面缺陷检测方法  

Surface defect detection method for aluminum profiles based on improved YOLOv7-tiny

作　　者：王浚银 文斌 沈艳军[1] 张俊 王子豪 WANG Junyin;WEN Bin;SHEN Yanjun;ZHANG Jun;WANG Zihao(School of Electrical and New Energy,China Three Gorges University,Yichang 443002,China;Hubei Provincial Engineering Technology Research Center for Power Transmission Line,Yichang 443002,China)

机构地区：[1]三峡大学电气与新能源学院,湖北宜昌443002 [2]湖北省输电线路工程技术研究中心,湖北宜昌443002

出　　处：《浙江大学学报(工学版)》2025年第3期523-534,共12页Journal of Zhejiang University:Engineering Science

基　　金：国家自然科学基金资助项目(62273200,61876097);湖北省输电线路工程技术研究中心(三峡大学)开放研究基金资助项目(2022KXL03);湖北省自然科学基金联合基金资助项目(2024AFD409).

摘　　要：针对铝型材表面缺陷具有种类多样、缺陷尺度差异大和小目标缺陷漏检的问题,提出改进的YOLOv7-tiny检测算法.利用残差结构、无参注意力机制(SimAM)、激活函数(FReLU)和裁剪卷积等重构空间金字塔池化模块,捕捉更多的细节信息,加强网络多尺度学习能力.优化检测层获取更多小目标特征和位置信息,提高网络多尺度缺陷检测能力.引入部分卷积替换高效层聚合网络(ELAN)中的3×3卷积建立轻量化模型,减少计算和训练负担.结合归一化Wasserstein距离(NWD)损失度量相似度,加速网络收敛并提升小目标缺陷检测能力.在天池铝型材数据集上进行测试,结果表明,改进YOLOv7-tiny算法在置信度阈值为0.25时,精确度达到95.0%,召回率达到91.8%,均值平均精度mAP@0.5达到94.5%,检测速度为45帧/s.相较于原算法,改进算法的mAP@0.5提高4.2个百分点,在脏点缺陷上的平均精度AP提高13.1个百分点;改进算法对于低分辨率图像和被干扰图像有更好的检测结果,表明其具备更好的泛化性和抗干扰能力.An improved YOLOv7-tiny detection algorithm was proposed to address the problems such as various types of surface defects in aluminum profiles,large differences in defect scales and missed detection of small target defects.The spatial pyramid pooling module was reconstructed by utilizing the residual structure,parameter-free attention mechanism(SimAM),activation function(FReLU)and clipping convolution to capture more detailed information and strengthen the multi-scale learning ability of the network.The optimized detection layer was used to obtain more small target features and location information,and improve the detection ability of network multi-scale defect.Partial convolution was introduced to replace the 3×3 convolution in the efficient layer aggregation network(ELAN),then the lightweight model was used to reduce the computing and training burden.Combined with the similarity of normalized Wasserstein distance(NWD)loss measurement,the network convergence was accelerated and the detection ability of small target defects was improved.Test was conducted on the Tianchi aluminium profile dataset,and the results showed that the improved YOLOv7-tiny algorithm achieved the accuracy,recall,mean average accuracy(mAP@0.5)and detection speed of 95.0%,91.8%,94.5%and 45 frames per second,respectively,when the confidence threshold was 0.25.Compared with the original algorithm,the mAP@0.5 of the improved algorithm was increased by 4.2 percentage point as a whole,the average accuracy(AP)of the dirty spot defect was increased by 13.1 percentage point;the detection results of the improved algorithm for low-resolution images and interfered images was better than of the original algorithm,which showed that the proposed method had better generalization and anti-interference ability.

关 键 词：铝型材 表面缺陷 小目标检测 SPPCSPC重构 残差结构 YOLOv7-tiny 归一化Wasserstein距离(NWD)损失 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术] TG146.21[自动化与计算机技术—计算机科学与技术]