[1] Wang Shuo, Zhang Liaojun, Yin Guojiang,. Defect identification method for steel surfaces based on improved YOLOv5 [J]. Journal of Southeast University (English Edition), 2024, 40 (1): 49-57. [doi:10.3969/j.issn.1003-7985.2024.01.006]

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Defect identification method for steel surfaces based on improved YOLOv5()

基于改进YOLOv5的钢材表面缺陷识别方法

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Journal of Southeast University (English Edition)[ISSN:1003-7985/CN:32-1325/N]

Volumn:

40

Issue:

2024 1

Page:

49-57

Research Field:

Computer Science and Engineering

Publishing date:

2024-03-20

Info

Title:

Defect identification method for steel surfaces based on improved YOLOv5

基于改进YOLOv5的钢材表面缺陷识别方法

Author(s):

Wang Shuo¹;  Zhang Liaojun²;  Yin Guojiang³

¹ College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, China
² College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing 210098, China
³ Safety Testing Center of Hydraulic Metal Structure of the Ministry of Water Resources, Hohai University, Nanjing 210098, China

王硕¹;  张燎军²;  尹国江³

¹ 河海大学土木与交通学院, 南京210098; ² 河海大学水利水电学院, 南京210098; ³ 河海大学水利部水工金属结构安全检测中心, 南京210098

Keywords:

steel;  defect detection;  convolutional neural network;  You Only Look Once(YOLO)

钢材;  缺陷检测;  卷积神经网络;  YOLO

PACS:

TP391.41;TP18;TG142

DOI:

10.3969/j.issn.1003-7985.2024.01.006

Abstract:

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.

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.

References:

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Memo

Memo:

Biographies: Wang Shuo(1992—), female, doctor; Zhang Liaojun(corresponding author), male, doctor, professor, ljzhang@hhu.edu.cn.
Foundation items: The Natural Science Foundation of Jiangsu Province(No. BK20230956), the Jiangsu Funding Program for Excellent Postdoctoral Talents(No. 2022ZB188), the Transportation Technology Plan Project of Jiangsu Province(No. 2020QD28).
Citation: Wang Shuo, Zhang Liaojun, Yin Guojiang. Defect identification method for steel surfaces based on improved YOLOv5[J].Journal of Southeast University(English Edition), 2024, 40(1):49-57.DOI:10.3969/j.issn.1003-7985.2024.01.006.

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