基于改进YOLOv5的钢板表面缺陷检测

doi:10.16180/j.cnki.issn1007-7820.2025.06.012

Abstract

Abstract:

To meet the requirements of steel plate surface defect detection in most industrial scenarios, a steel plate surface defect detection algorithm based on improved YOLOv5(You Only Look Once version 5) is proposed to solve the problems such as low detection accuracy of steel plate surface defects and failure to detect small target defects. On the basis of YOLOv5, the CBAM(Convolution Block Attention Module) is embedded into the backbone network to improve network detection accuracy. The context enhancement module is added to improve the detection performance of small targets. The NWD(Normalized Wasserstein Distance) metric is used to replace the original IoU(Intersection over Union) metric in YOLOv5, making the network more accurate in identifying crack defects. Experimental results show that the average detection accuracy of the proposed steel plate surface defect detection algorithm for six types of defects, including crack, inclusion, plaque, pitting, pressed iron oxide, scratch, reaches 88.9%, the frame rate reaches 110.4 frame·s^-1, and the accuracy of small target crack reaches 75%.

Key words: steel plate surface defect detection, YOLOv5, attention module, context enhancement module, small goals, position deviation, NWD measurement, IoU measurement

CLC Number:

TP391

SHEN Tingqian, LU Yujun, XIN Hao, WU Hanchao, WANG Shinan. Steel Plate Surface Defect Detection Based on Improved YOLOv5[J].Electronic Science and Technology, 2025, 38(6): 82-88.

Figures/Tables 13

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References 26

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对比模型	YOLOv5	YOLOv5- CAM	YOLOv5- CAM-NWD	YOLOv5-CAM- NWD-CBAM
裂纹AP/%	69.1	71.3	73.9	75.0
夹杂AP/%	89.9	89.9	90.4	90.3
斑块AP/%	96.0	96.3	96.6	96.9
麻点AP/%	86.3	87.4	87.6	88.0
压入氧化铁皮AP/%	84.2	85.3	86.5	86.7
划痕AP/%	95.0	96.1	96.2	96.4
mAP/%	86.8	87.7	88.5	88.9
FPS/frame·s^-1	114.0	108.7	111.1	110.4

算法	mAP/%	FPS/frame·s^-1
Faster-RCNN	69.9	35.5
SSD	73.4	63.8
YOLOX	78.0	51.3
YOLOv3	90.8	47.0
YOLOv5	86.8	114.0
本文算法	88.9	110.4

Steel Plate Surface Defect Detection Based on Improved YOLOv5

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