Deep Morphological Anomaly Detection Based on Angular Margin Loss

Taehyeon Kim,Yoonsik Choe,Eungi Hong

doi:10.3390/app11146545

Abstract

Deep anomaly detection aims to identify “abnormal” data by utilizing a deep neural network trained on a normal training dataset. In general, industrial visual anomaly detection systems distinguish between normal and “abnormal” data through small morphological differences such as cracks and stains. Nevertheless, most existing algorithms emphasize capturing the semantic features of normal data rather than the morphological features. Therefore, they yield poor performance on real-world visual inspection, although they show their superiority in simulations with representative image classification datasets. To address this limitation, we propose a novel deep anomaly detection algorithm based on the salient morphological features of normal data. The main idea behind the proposed algorithm is to train a multiclass model to classify hundreds of morphological transformation cases applied to all the given data. To this end, the proposed algorithm utilizes a self-supervised learning strategy, making unsupervised learning straightforward. Additionally, to enhance the performance of the proposed algorithm, we replaced the cross-entropy-based loss function with the angular margin loss function. It is experimentally demonstrated that the proposed algorithm outperforms several recent anomaly detection methodologies in various datasets.

Highlights

In data analysis, anomaly detection refers to the identification of outliers in a data distribution [1]
Several visual anomaly detection algorithms based on deep neural networks (DNNs) have been proposed, including variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs)
All experimental results are reported as the area under the receiver operating characteristic (AUROC), which is a useful performance metric to measure the quality of the trade-off of g(A) in (8)

Summary

Introduction

Anomaly detection refers to the identification of outliers in a data distribution [1]. Several visual anomaly detection algorithms based on deep neural networks (DNNs) have been proposed, including variational autoencoders (VAEs), convolutional neural networks (CNNs), and generative adversarial networks (GANs). DNNs can merely access the “normal” class instances. Most studies focused on representing or extracting salient features from normal instances by utilizing various methodologies, such as low-dimensional embedding, data reconstruction, and self-supervised learning. Deep anomaly detection (DAD) methodologies primarily involve the extraction of the semantically salient features of “normal” images using DNNs. most studies reported excellent results on representative image classification datasets composed of semantically distinguishable classes, e.g., CIFAR-10 [2], Fashion-MNIST [3], and cats-anddogs dataset [4]. The semantic difference in the image domain leads to large morphological differences, such as outline and texture.

Methods

Results

Conclusion