A Method for Surface Defect Detection in Resistance Spot Welding Based on an Improved DeepLabv3+ Model

doi:10.3969/j.issn.1004-132X.2026.09.025

Abstract

Abstract:

Aiming at the issues that machine vision technology was sensitive to factors such as oil stains， reflections， and curved surfaces on resistance spot welding surfaces， making image processing difficult and segmentation accuracy low， a semantic segmentation method was proposed based on deep learning. By improving the DeepLabv3+ model， intelligent segmentation， recognition， and classification of welding spot image features were achieved. Experimental results show that： using MobileNetV3-Large as the backbone network model lightweight is achieved. Adding an improved CBAM-change attention module is the model's segmentation performance is further enhanced. The improved DeepLabv3+ model achieves a mean intersection over union of 92.15%， an F1 score of 95.86%， an accuracy of 99.81%， and an average processing time of 26.24 ms per welding spot image， enabling precise segmentation of white body welding spot defects.

Key words: resistance spot welding, semantic segmentation, DeepLabv3+model, defect detection

摘要：

针对机器视觉技术对电阻点焊表面的油污、反光、曲面等因素敏感，使得图像处理困难和分割精度低等问题，提出一种基于深度学习的语义分割方法，通过改进DeepLabv3+模型实现对焊点图像特征的智能分割、识别与分类。实验结果表明：采用MobileNetV3-Large作为主干网络实现了模型轻量化；添加改进的CBAM-change注意力模块提高了模型的分割性能；改进后的DeepLabv3+模型的平均交并比达到92.15%、F1分数达到95.86%、准确率达到99.81%、处理单张焊点图像的平均时间为26.24 ms，可实现白车身焊点缺陷精准分割。

关键词: 电阻点焊, 语义分割, DeepLabv3+模型, 缺陷检测

CLC Number:

TG438

XIONG Xin, WU Wei, DU Jie, XIANG Cipei. A Method for Surface Defect Detection in Resistance Spot Welding Based on an Improved DeepLabv3+ Model[J]. China Mechanical Engineering, 2026, 37(9): 2326-2333.

熊鑫, 吴玮, 杜洁, 向词裴. 基于改进DeepLabv3+模型的电阻点焊表面缺陷检测方法[J]. 中国机械工程, 2026, 37(9): 2326-2333.

Figures/Tables 16

Fig.1 Technology roadmap

Fig.2 Experimental equipment

Tab.1 Experimental process parameters

参数名称	数值范围
电流/kA	6~12
时间/ms	100~300
压力/kN	5~7

Fig.3 Welding spot image dataset

Fig.4 DeepLabv3+ network architecture［14］

Fig.5 Improved DeepLabv3+ network structure

Fig.6 MobileNetV3 structure

Fig.7 Convolutional block attention module

Fig.8 Channel attention module and spatial attention module

Fig.9 Comparison of activation functions

Fig.10 Loss curve and mIoU curve

Tab.2 Comparison results of model improvement

模型		I_mIoU/%	F1分数/%	准确率A/%	时间t/ms
1	图10a	91.79	95.64	99.80	31.37
2	图10b	87.79	93.99	99.70	21.07
3	图10c	90.17	94.82	99.77	26.09
4	图10d	91.06	95.26	99.78	26.38
5	图10e	92.15	95.86	99.81	26.24

Fig.11 Visualization of different models

Tab.3 Comparison of results of different models

模型	I_mIoU/%	F1分数/%	准确率A/%	参数量/10⁶	GFLOPs
U-Net	92.13	95.89	99.82	43.934	184.737
PSPNet	88.53	93.82	99.70	2.377	6.034
HrNet	92.72	96.17	99.83	9.642	37.487
DeepLabv3+（Ours）	92.15	95.86	99.81	4.923	16.700

Fig.12 Loss curve and mIoU curve of RSW-D

Fig.13 Visualization of RSW-D

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