Review of Machine Vision Detection Methods for Texture Surface Defects
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摘要: 纹理表面缺陷检测在机器视觉领域具有意义和挑战性，其历史可以追溯到20世纪中后期，近年来随着深度学习技术的蓬勃发展，纹理表面缺陷检测技术大幅飞跃。直至今日，关于纹理表面缺陷检测的调研和综述仍然很少。在此背景下，本文回顾2017年-2021年间200余篇纹理表面缺陷机器视觉检测论文，对纹理表面缺陷机器视觉检测研究进展进行了及时、全面的调查；分析了纹理表面缺陷检测的发展历史和最新研究进展，原则上将纹理表面缺陷机器视觉检测方法分为传统方法与深度学习方法，并对二者进行了深层次研究分析，特别是深度学习方法；对近期出现的几种纹理表面缺陷机器视觉检测方法主题进行总结的同时，也对这些主题的研究进展进行了综述。最后，对未来的研究趋势进行了展望，以期为后续研究提供指导和启示。Abstract: Texture surface defect detection is meaningful and challenging in the field of machine vision. The history of texture surface defect detection can be traced back to the middle to late 20th century. Moreover, in recent years, with the flourishing development of deep learning technology, texture surface defect detection technology had a big leap. However, so far, there are still few surveys and reviews of texture surface defect detection. Against such a background, we comprehensively reviewed more than 200 papers about texture surface defect detection with machine vision from 2017 to 2021 and made a timely and comprehensive investigation of its research progress. This paper reviews the development history and latest research progress of texture surface defect detection. In principle, the methods of texture surface defect detection by machine vision are divided into the traditional method and the deep learning method, which were studied and analyzed deeply, especially the deep learning method. The paper summarizes several methods of texture surface defect detection by machine vision that appeared recently and reviews the research progress of these methods. Finally, it introduced the future research trends to provide enlightenment for further studies.
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Key words:
- texture /
- defect detecting /
- machine vision /
- machine learning /
- deep learning
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图 2 纹理表面缺陷视觉检测方法分类框架图
Figure 2. Classification framework of visual detection methods for texture surface defects
表 1 分类结果混淆矩阵
Table 1. Confusion matrix for the classification results
真实情况 预测结果 正例 反例 正例 PT NF 反例 PF NT 
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方法 方法描述 F1指标/% RAccuracy% 方法分类 发表年份 Yu等[100] 基于两阶段全卷积FCN网络的表面缺陷检测方法 95.99 基于语义分割方法 2017 Wang等[76] 基于11层卷积神经网络的表面缺陷检测方法 99.80 基于传统CNN方法 2018 Zhou等[175] 基于双VGG16网络的表面缺陷检测方法 99.49 迁移学习方法 2019 Enshaei等[115] 使用粗略标记的弱监督U-net表面缺陷检测方法 79.33 99.16 弱监督学习方法 2020 Chen等[118] 基于注意力结构卷积神经网络的纹理表面缺陷检测方法 74.46 99.85 基于弱监督学习方法 2020 Zhang等[216] 基于弱监督学习的分类感知对象检测方法 65.8 87.60 基于知识蒸馏 2021 Tsai等[152] 基于改进的CAE模型的纹理表面缺陷检测方法 95.00 基于无缺陷样本训练方法 2021
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方法 方法描述 RAccuracy% 方法分类 发表年份 Schlegl等[137] 基于GAN的缺陷检测方法AnoGAN 55 基于无缺陷样本
训练方法2017 Bergmann等[154] 基于结构相似性感知损失SSIM卷积自编码器AE网络的表面缺陷检测方法 63 基于无缺陷样本
训练方法2019 Venkataramanan等[156] 基于引导注意对抗变分自编码器GAVGA的缺陷检测方法（无监督） 78 基于无缺陷样本
训练方法2020 Wang等[150] 基于VQ-VAE的表面缺陷检测方法 85 基于无缺陷样本
训练方法2020 Tsai等[152] 基于改进CAE模型的纹理表面缺陷检测方法 91 基于无缺陷样本
训练方法2021 Tellaeche等[148] 基于卷积自编码器与OC-SVM的纹理表面缺陷检测方法 92 基于无缺陷样本
训练方法2021
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表 4 纹理表面缺陷机器视觉检测方法优缺点比较
Table 4. Comparison of the pros and cons of machine vision detection methods for texture surface defects
分类 方法 优点 缺点 传统方法 图像结构方法 方法原理简单 多数方法泛化能力差，仅适用于某一种特定情况下的缺陷检测 频域分析方法 在空间域较难分离的特征在频域可分离性提升 频域分析方法对噪声敏感，多数情况下计算复杂，耗时较长 传统机器学习方法 缺陷检测效果优于其他传统方法；与深度学习方法相比，可解释性强 在实际使用过程中，超参数对检测效果影响巨大 深度学习方法 监督学习方法 具有较高的缺陷检测精度，是目前缺陷检测效果最好的方法 需要大量标注正确的训练样本 弱监督学习方法 基于存在标注问题样本进行训练，方法可达到较高精度 无过明显缺陷，属于监督学习与无监督学习方法的折中 无监督学习方法 模型训练简单，仅使用无标记样本训练，不需要标注 缺陷检测精度相对监督学习方法较低 迁移学习方法 使用其他领域数据训练模型，减少了对缺陷样本的需求 在不同预训练数据集下效果差异较大，存在负迁移、负适配问题 主动学习方法 可以在样本标注较少情况下进行训练 模型训练需要人工与机器迭代，虽然减少了标注样本数量，但标注代价并没有过多下降
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