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摘要: 图像异常检测是计算机视觉领域的一个热门研究课题, 其目标是在不使用真实异常样本的情况下, 利用现有的正常样本构建模型以检测可能出现的各种异常图像, 在工业外观缺陷检测、医学图像分析、高光谱图像处理等领域有较高的研究意义和应用价值. 本文首先介绍了异常的定义以及常见的异常类型. 然后, 本文根据在模型构建过程中有无神经网络的参与, 将图像异常检测方法分为基于传统方法和基于深度学习两大类型, 并分别对相应的检测方法的设计思路、优点和局限性进行了综述与分析. 其次, 梳理了图像异常检测任务中面临的主要挑战. 最后, 对该领域未来可能的研究方向进行了展望.Abstract: Image anomaly detection is a hot research topic in the field of computer vision, which aims at training a model by normal images to detect potential anomalous images without requiring any real anomalous images for training. Therefore, it has high research and application value in the fields of industrial surface defect detection, medical image analysis, and hyperspectral image processing. This paper firstly introduces the definition of anomaly and its common type. Secondly, image anomaly detection methods are divided into traditional and deep learning-based methods according to whether the neural network is involved. Meanwhile, the design ideas, advantages and limitations of these methods are summarized and analyzed respectively. Thirdly, the major challenges faced in image anomaly detection are introduced. Finally, the future directions of image anomaly detection are discussed.
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Key words:
- Image anomaly detection /
- computer vision /
- deep learning /
- neural network /
- background reconstruction
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图 20 需要构建负样本的几种方法的示意图
Fig. 20 The graphical illustration of the methods based on creating fake-negative samples
表 2 基于传统方法的图像异常检测技术的分类和特点
Table 2 The classification and characteristic of traditional image anomaly detection methods
方法类别 设计思路 优点 缺点 参考文献 模板匹配 建立待测图像和模板图像之间的对应关系, 通过比较得到异常区域 方法简单有效, 对于采集环境高度可控的场景有很高的检测精度 不适用于多变的场景或目标 [29−48] 统计模型 通过统计学方法构建背景模型 具有详实的理论基础和推导过程, 检测速度快 需要大量的训练样本, 且仅适用于一些简单背景下的异常检测 [49−54] 图像分解 将原始图像分解成代表背景的低秩矩阵和代表异常区域的稀疏矩阵 具有详实的理论基础且无需训练过程 速度较慢, 而且不适合在结构复杂的图像中进行异常检测 [56−61] 频域分析 通过编辑图像的频谱信息来消除图像中重复的背景纹理部分以凸显异常区域 无需训练过程, 检测速度很快 还需更详实的理论论证, 且仅适用于一些有重复性纹理的图像, 通用性较差 [64−70] 稀疏编码重构 借助稀疏编码和字典学习等方式学习正常样本的表示方法, 从重构误差和稀疏度等角度检测异常 适用于各种类型的图像, 通用性很好 检测时间长, 而且需要额外的空间保存过完备的字典. [71−78] 分类面构建 建立分类面将现有的正常样本和潜在的异常样本进行区分 通用性较好, 且速度较快 各项参数的选择过程较为复杂 [81−87] 
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表 3 基于深度学习的图像异常检测技术的分类和特点
Table 3 The classification and characteristic of deep learning based image anomaly detection
方法类别 设计思路 优点 缺点 参考文献 距离度量 将正常图像映射到指定区域内, 并减小正常特征之间距离, 根据待测图像的特征到聚类中心的距离进行异常检测 模型结构简单, 适用范围广 模型可能出现退化, 需要设计额外的辅助任务, 且无法准确定位异常区域 [88−98] 分类面构建 通过几何变换增广现有数据, 直接训练分类模型并利用置信度来检测异常 模型训练较为简单, 语义信息提取能力更强, 异常检测精度很高 几何变换的操作在纹理图像等场景下并不适用 [101−102] 寻找与正常样本近似的图像作为负样本来训练二分类网络, 构建正常图像与潜在异常图像间的分类面 应用场景广泛, 异常检测精度高 需要精心设计损失函数和生成的负样本, 模型设计复杂 [104−117] 图像重构 利用自编码器等模型学习正常图像的表达方式, 并根据待测图像的重构误差来进行异常检测 训练阶段无需引入额外的样本, 且应用场景广泛, 速度较快 一般的方法重构结果较为模糊, 且缺乏更为高效可靠的方法避免重构出异常区域 [118−134] 利用GAN来获得更为清晰的图像重构效果 应用场景广泛, 异常区域定位精度高 模型训练复杂, 而且缺乏理论上的保证 [135−147] 结合传统方法 利用预训练的网络或者自编码器模型对图像进行特征提取, 在决策阶段利用传统方法进行异常检测 相比传统方法精度更高通用性更好, 且速度较快 在检测精度上略有不足 [150−160]
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表 5 各图像异常定位方法在MVTec AD上的性能
Table 5 Performance of image anomaly localization methods on MVTec AD
方法 大致思路 定位性能 AUROC PRO-score AE[146] 利用自编码器进行图像重构 0.817 0.790 AnoGAN[15] 利用GAN中的生成器进行图像重构 0.743 0.443 Iterative Projection[134] 在图像重构基础上采用迭代优化寻找最优的正常图像 0.893 — AESc[172] 利用蒙特卡洛对重构网络进行Dropout并利用预测不确定性进行异常定位 0.86 — P-Net[16] 在图像重构过程中添加对纹理结构的约束 0.89 — Uninformed Students[97] 联合考虑待测图特征到目标特征之间的距离和方差进行异常定位 — 0.857 CAVGA[144] 在图像重构的基础上采用注意力图定位异常区域 0.93 — FCDD[96] 利用全卷积网络提取特征并以偏置项作为特征映射中心 0.96 — Patch SVDD[173] 计算待检图像片和最近似的正常图像片之间的距离进行异常定位 0.957 — PaDiM[171] 用预训练的网络进行特征提取, 利用多维高斯模型进行异常定位 0.975 0.921 SPADE[174] 寻找待测样本的K-近邻正常图像作为参考, 再通过距离度量进行异常检测 0.965 0.917
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