首页|基于改进YOLOv5的钢材表面缺陷识别方法

基于改进YOLOv5的钢材表面缺陷识别方法

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由于传统的机器视觉检测方法在小尺度钢材表面缺陷识别中存在检测精度较差的问题,提出了一种基于改进 YOLOv5算法的钢材表面缺陷无损识别方法.将 Res2Block模块应用于 YOLOv5算法的骨干,在扩大感受野的同时提高计算效率;在YOLOv5算法的颈部融合gnConv结构,以提高表面缺陷识别方法的计算性能.为验证所提方法的有效性,进行了不同模块组合的消融试验,并与其他目标检测方法进行了对比.结果表明:所提方法在钢材表面缺陷识别中实现了 67.8％的mAP和86.0％的F1值;与原始YOLOv5算法相比,所提方法在小尺度钢材表面缺陷识别方面表现更为优越;与其他目标检测方法如SSD、YOLOv3、YOLOv5-Lite、YOLOv8相比,所提方法的计算精度有明显的提高.
Defect identification method for steel surfaces based on improved YOLOv5
Traditional machine vision detection methods suffer from low accuracy in identifying small-scale defects.To address this,a nondestructive identification method for steel surface defects is proposed based on an enhanced version of the fifth version of the You Only Look Once(YOLOv5)algorithm.In this improved approach,the Res2Block module is incorporated into the backbone of the YOLOv5 algorithm to expand the receptive field and improve computational efficiency.Additionally,the recursive gated convolution structure is fused into the neck of the YOLOv5 algorithm to further enhance the computational performance of the surface defect identification method.To validate the effectiveness of the proposed method,a series of ablation experiments were conducted using different module combinations.These results were then compared with those obtained through other object detection methods.This comparison reveals that the proposed method achieves a mean average precision of 67.8％and an F1-score of 86.0％in steel surface defect identification.When compared with the original YOLOv5 algorithm,the proposed method exhibits superior performance,particularly in the identification of small-scale steel surface defects.Furthermore,it also surpasses other object detection methods,such as SSD,YOLOv3,YOLOv5-Lite,and YOLOv8,demonstrating significant improvements in computational accuracy.

steeldefect detectionconvolutional neural networkYou Only Look Once(YOLO)

王硕、张燎军、尹国江

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河海大学土木与交通学院,南京 210098

河海大学水利水电学院,南京 210098

河海大学水利部水工金属结构安全检测中心,南京 210098

钢材 缺陷检测 卷积神经网络 YOLO

Natural Science Foundation of Jiangsu ProvinceJiangsu Funding Program for Excellent Postdoctoral TalentsTransportation Technology Plan Project of Jiangsu Province

BK202309562022ZB1882020QD28

2024

10.3969/j.issn.1003-7985.2024.01.006

东南大学学报(英文版)
东南大学

东南大学学报(英文版)

影响因子：0.211
ISSN：1003-7985
年,卷(期)：2024.40(1)
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