改进YOLOv10的钢材表面缺陷检测方法  

Improving the steel surface defect detection method of YOLOv10

作　　者：吴葛 朱宇凡 叶天成 Wu Ge;Zhu Yufan;Ye Tiancheng(School of Electrical&Electronic Engineering,Huazhong University of Science and Technology,Wuhan 430074,China)

机构地区：[1]华中科技大学电气与电子工程学院,武汉430074

出　　处：《电子测量技术》2025年第4期158-168,共11页Electronic Measurement Technology

基　　金：教育部2020年第二批新工科研究与实践项目(E-NYDQHGC20202219)资助。

摘　　要：针对工业系统现有钢材表面缺陷检测模型较老,准确率有限,存在错判漏判的问题,提出了一种改进YOLOv10的钢材表面缺陷检测方法FAA-YOLO。该方法通过引入轻量化网络FasterNet和多尺度注意力机制EMA,设计了C2f_Faster_EMA模块,实现Backbone网络轻量化与特征提取能力增强的平衡;在Backbone网络末端添加自适应细粒度通道注意力机制AFGC,增强模型骨干网络对输入特征的初步提取能力,进而提高模型的检测精度;将Neck部分换为注意力尺度序列融合框架ASF,提升模型整合多尺度特征信息的能力。在NEU-DET钢材表面缺陷数据集上进行对比实验和消融实验,实验结果表明,提出的FAA-YOLO模型较基线YOLOv10n模型参数量降低11.01%,计算量降低7.69%,检测精度提高2.9个点,达到83.6%的检测准确率,在降低模型复杂度的同时实现了较高的检测准确率,在工业系统中具有高可用性与高实时性。Aiming at the issues of outdated existing models for steel surface defect detection,limited accuracy,and the existence of misjudgment and omission,an improved YOLOv10 method for steel surface defect detection,named FAA-YOLO,is proposed.This method introduces the lightweight network FasterNet and multi-scale attention mechanism EMA,designing the C2f_Faster_EMA module to balance the lightweight of the backbone network with enhanced feature extraction capabilities.An adaptive fine-grained channel attention mechanism AFGC is added at the end of the backbone network to enhance the preliminary feature extraction ability of the model′s backbone network,thereby improving the model′s detection accuracy.The Neck part is replaced with an attention scale sequence fusion framework ASF to enhance the model′s ability to integrate multi-scale feature information.Comparative experiments and ablation experiments on the NEU-DET steel surface defect dataset show that the proposed FAA-YOLO model reduces the number of parameters by 11.01%,the computational load by 7.69%,and increases the detection accuracy by 2.9 percentage points,achieving a detection accuracy of 83.6%.This method reduces the complexity of the model while achieving high detection accuracy,demonstrating high usability and real-time performance in industrial systems.

关 键 词：钢材表面缺陷检测 YOLOv10 FasterNet EMA AFGC ASF-YOLO 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术] TN791[自动化与计算机技术—计算机科学与技术]