基于改进ACGAN的钢表面缺陷视觉检测方法

doi:10.3901/JME.2022.24.032

机械工程学报 ›› 2022, Vol. 58 ›› Issue (24): 32-40.doi: 10.3901/JME.2022.24.032

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基于改进ACGAN的钢表面缺陷视觉检测方法

李可¹, 祁阳¹, 宿磊¹, 顾杰斐¹, 苏文胜²

1. 江南大学机械工程学院无锡 214122;
2. 江苏省特种设备安全监督检验研究院南京 210003

收稿日期:2022-01-20 修回日期:2022-09-03 出版日期:2022-12-20 发布日期:2023-04-03
通讯作者: 宿磊(通信作者),男,1986年出生,博士,副教授,硕士研究生导师。主要研究方向为微电子封装无损检测、机器视觉、故障诊断。E-mail:lei_su2015@jiangnan.edu.cn
作者简介:李可,男,1978年出生,博士,教授,博士研究生导师。主要研究方向为机器视觉、信号处理、故障诊断。E-mail:like_jiangnan@163.com;宿磊(通信作者),男,1986年出生,博士,副教授,硕士研究生导师。主要研究方向为微电子封装无损检测、机器视觉、故障诊断。E-mail:lei_su2015@jiangnan.edu.cn
基金资助:
国家自然科学基金（51775243，11902124）和江苏省市场监督管理局科技计划（KJ196043）资助项目。

Visual Inspection of Steel Surface Defects Based on Improved Auxiliary Classification Generation Adversarial Network

LI Ke¹, QI Yang¹, SU Lei¹, GU Jiefei¹, SU Wensheng²

1. School of Mechanical Engineering, Jiangnan University, Wuxi 214122;
2. Special Equipment Safety Supervision Inspection Institute of Jiangsu Province, Nanjing 210003

Received:2022-01-20 Revised:2022-09-03 Online:2022-12-20 Published:2023-04-03

摘要/Abstract

摘要： 为提高小样本环境下钢表面缺陷检测精度，提出一种基于改进辅助分类生成对抗网络（Auxiliary classifier generative adversarial network，ACGAN）的钢表面缺陷检测方法。利用残差块优化ACGAN的网络结构，提高模型的特征提取能力；其次，为提高模型训练的稳定性，在网络的卷积层中添加谱范数归一化，防止模型异常的梯度变化；基于正-未标记分类的思想优化判别器的损失函数，提高生成样本的质量；同时，为缓解生成对抗网络的模式崩塌问题，在损失函数中添加梯度惩罚来约束判别器的梯度；通过生成器和判别器的对抗优化训练实现样本扩充。通过对钢表面缺陷数据集的试验，验证了提出的方法能准确有效地实现小样本环境下钢表面缺陷检测。与经典的SVM、ResNet50以及一些小样本分类模型相比，所提方法具有更高的检测精度。

关键词: 钢表面缺陷检测, 辅助分类生成对抗网络, 小样本, 梯度惩罚

Abstract: In order to improve the accuracy of steel surface defect detection in small sample environment, a new method of steel surface defect detection based on the improved auxiliary classifier generative adversarial network (ACGAN) is proposed. Firstly, the residual block is used to optimize the network of ACGAN to improve the feature extraction ability of the model; Secondly, in order to improve the stability of model training, spectral norm normalization is added to the convolution layer of the network to prevent abnormal gradient changes of the model. Then, the loss function of discriminator is optimized based on positive-unlabeled classification to improve the quality of generated samples. At the same time, a gradient penalty is added to the loss function to constrain the gradient of the discriminator in order to alleviate the mode collapse of the Generative Adversarial Network. Finally, the sample expansion is realized through the adversarial optimization training of generator and discriminator. We conducted experiments on steel surface defect datasets to validate the proposed method can accurately and effectively detect steel surface defects in a small sample environment. Compared with the classical support vector machine, ResNet50 and some small sample classification models, the proposed method has higher detection accuracy.

Key words: steel surface defect detection, auxiliary classification generation adversarial network, small sample, gradient penalty

中图分类号:

TP29

李可, 祁阳, 宿磊, 顾杰斐, 苏文胜. 基于改进ACGAN的钢表面缺陷视觉检测方法[J]. 机械工程学报, 2022, 58(24): 32-40.

LI Ke, QI Yang, SU Lei, GU Jiefei, SU Wensheng. Visual Inspection of Steel Surface Defects Based on Improved Auxiliary Classification Generation Adversarial Network[J]. Journal of Mechanical Engineering, 2022, 58(24): 32-40.

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基于改进ACGAN的钢表面缺陷视觉检测方法

Visual Inspection of Steel Surface Defects Based on Improved Auxiliary Classification Generation Adversarial Network

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