Defect detection in the manufacturing industry is of utmost importance for product quality inspection. Recently, optical defect detection has been investigated as an anomaly detection using different deep learning methods. However, the recent works do not explore the use of point pattern features, such as SIFT for anomaly detection using the recently developed set-based methods. In this paper, we present an evaluation of different point pattern feature detectors and descriptors for defect detection application. The evaluation is performed within the random finite set framework. Handcrafted point pattern features, such as SIFT as well as deep features are used in this evaluation. Random finite set-based defect detection is compared with state-of-the-arts anomaly detection methods. The results show that using point pattern features, such as SIFT as data points for random finite set-based anomaly detection achieves the most consistent defect detection accuracy on the MVTec-AD dataset.
Value-order Decomposition for Generalist Anomaly Detection
Dalian University of Technology（大连理工大学）；Xi’an Chang’an Vanke City Primary School（西安长安万科城小学）；Key Laboratory of Social Computing and Cognitive Intelligence (Dalian University of Technology), Ministry of Education（社会计算与认知智能教育部重点实验室（大连理工大学））
JR East Information Systems（东日本旅客铁道信息系统）；PARC, A Xerox Company（施乐帕克研究中心）
Although training data is essential for machine learning, railway companies are facing difficulties in gathering adequate images of defective equipment due to their proactive replacement of would be defective equipment. Nevertheless, proactive replacement is indispensable for safe and undisturbed operation of public transport. In this research, we have developed a model using CycleGAN to generate artificial images of defective equipment instead of real images. By adopting these generated images as training data, we verified that these images are indistinguishable from real images and they play…
A Unified Anomaly Synthesis Strategy with Gradient Ascent for Industrial Anomaly Detection and Localization
Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；CASI Vision Technology CO., LTD.（CASI视觉技术有限公司）
MathPhys-Guided Coarse-to-Fine Anomaly Synthesis with SQE-Driven Bi-Level Optimization for Anomaly Detection
Foundation Model Research Center, Institute of Automation, Chinese Academy of Sciences（中国科学院自动化研究所基础模型研究中心）；School of Artificial Intelligence, University of Chinese Academy of Sciences（中国科学院大学人工智能学院）；Objecteye Inc.（Objecteye公司）
We aim at constructing a high performance model for defect detection that detects unknown anomalous patterns of an image without anomalous data. To this end, we propose a two-stage framework for building anomaly detectors using normal training data only. We first learn self-supervised deep representations and then build a generative one-class classifier on learned representations. We learn representations by classifying normal data from the CutPaste, a simple data augmentation strategy that cuts an image patch and pastes at a random location of a large image. Our empirical study on MVTec anoma…
Royal Melbourne Institute of Technology（皇家墨尔本理工大学）
In this paper, we propose an efficient approach for industrial defect detection that is modeled based on anomaly detection using point pattern data. Most recent works use \textit{global features} for feature extraction to summarize image content. However, global features are not robust against lighting and viewpoint changes and do not describe the image's geometrical information to be fully utilized in the manufacturing industry. To the best of our knowledge, we are the first to propose using transfer learning of local/point pattern features to overcome these limitations and capture geometrica…
Anomaly detection refers to the task of finding unusual instances that stand out from the normal data. In several applications, these outliers or anomalous instances are of greater interest compared to the normal ones. Specifically in the case of industrial optical inspection and infrastructure asset management, finding these defects (anomalous regions) is of extreme importance. Traditionally and even today this process has been carried out manually. Humans rely on the saliency of the defects in comparison to the normal texture to detect the defects. However, manual inspection is slow, tedious…
Synthesis4AD: Synthetic Anomalies are All You Need for 3D Anomaly Detection
National Center of Technology Innovation for Intelligent Design and Numerical Control, Huazhong University of Science and Technology（华中科技大学国家智能设计与数控技术创新中心）；School of Artificial Intelligence and Robotics, Hunan University（湖南大学人工智能与机器人学院）；Department of Intelligent Manufacturing, Contemporary Amperex Technology Ltd.（宁德时代新能源科技股份有限公司智能制造部）；Institute for Infocomm Research, A*STAR（新加坡科技研究局资讯通信研究院）；School of Electrical and Electronic Engineering, Nanyang Technological University（南洋理工大学电气与电子工程学院）
Attention Modules Improve Image-Level Anomaly Detection for Industrial Inspection: A DifferNet Case Study
Voxar Labs, Centro de Informática, Universidade Federal de Pernambuco（佩南布科联邦大学信息学中心 Voxar 实验室）；Visual Computing Lab, DC, Universidade Federal Rural de Pernambuco（佩南布科联邦农村大学 DC 视觉计算实验室）；Chemnitz University of Technology（开姆尼茨工业大学）
In industrial vision, the anomaly detection problem can be addressed with an autoencoder trained to map an arbitrary image, i.e. with or without any defect, to a clean image, i.e. without any defect. In this approach, anomaly detection relies conventionally on the reconstruction residual or, alternatively, on the reconstruction uncertainty. To improve the sharpness of the reconstruction, we consider an autoencoder architecture with skip connections. In the common scenario where only clean images are available for training, we propose to corrupt them with a synthetic noise model to prevent the …
Harokopio University of Athens, Athens, Greece（雅典哈尔科波利欧大学）；National and Kapodistrian University of Athens, Athens, Greece（雅典国家与卡波迪斯蒂亚纳大学）；CentraleSupelec, University Paris Saclay, Paris, France（巴黎萨克雷中央苏普尔大学）；Archimedes, Athena Research Center, Athens, Greece（阿提卡研究所以及阿奇米德中心）
Anomaly Detection by Effectively Leveraging Synthetic Images
Department of Electrical and Computer Engineering（电气与计算机工程系）；Department of Artificial Intelligence（人工智能系）；Department of Architecture（建筑系）；Department of Radiation Physics（放射物理系）
Leibniz University Hannover（汉诺威莱布尼茨大学）；Linköping University（林雪平大学）
Industrial defect detection is commonly addressed with anomaly detection (AD) methods where no or only incomplete data of potentially occurring defects is available. This work discovers previously unknown problems of student-teacher approaches for AD and proposes a solution, where two neural networks are trained to produce the same output for the defect-free training examples. The core assumption of student-teacher networks is that the distance between the outputs of both networks is larger for anomalies since they are absent in training. However, previous methods suffer from the similarity of…
Western University（韦仕敦大学）；National Research Council Canada（加拿大国家研究委员会）
Surface anomaly detection plays an important quality control role in many manufacturing industries to reduce scrap production. Machine-based visual inspections have been utilized in recent years to conduct this task instead of human experts. In particular, deep learning Convolutional Neural Networks (CNNs) have been at the forefront of these image processing-based solutions due to their predictive accuracy and efficiency. Training a CNN on a classification objective requires a sufficiently large amount of defective data, which is often not available. In this paper, we address that challenge by…
A Novel Representation of Periodic Pattern and Its Application to Untrained Anomaly Detection
The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））；The Hong Kong University of Science and Technology（香港科技大学）；Guangzhou HKUST Fok Ying Tung Research Institute（广州香港科技大学霍英东研究院）
Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization
Jiangxi Normal University（江西师范大学）；Southern University of Science and Technology（南方科技大学）；Swansea University（斯旺西大学）；Zhejiang University（浙江大学）；Anhui University（安徽大学）；Northwestern Polytechnical University（西北工业大学）
Humans can easily detect a defect (anomaly) because it is different or salient when compared to the surface it resides on. Today, manual human visual inspection is still the norm because it is difficult to automate anomaly detection. Neural networks are a useful tool that can teach a machine to find defects. However, they require a lot of training examples to learn what a defect is and it is tedious and expensive to get these samples. We tackle the problem of teaching a network with a low number of training samples with a system we call AnoNet. AnoNet's architecture is similar to CompactCNN wi…
This paper addresses the problem of defect segmentation in semiconductor manufacturing. The input of our segmentation is a scanning-electron-microscopy (SEM) image of the candidate defect region. We train a U-net shape network to segment defects using a dataset of clean background images. The samples of the training phase are produced automatically such that no manual labeling is required. To enrich the dataset of clean background samples, we apply defect implant augmentation. To that end, we apply a copy-and-paste of a random image patch in the clean specimen. To improve the robustness of the…
Defect-aware Hybrid Prompt Optimization via Progressive Tuning for Zero-Shot Multi-type Anomaly Detection and Segmentation
Corporate Research, Robert Bosch GmbH（罗伯特·博世集团企业研究部）；Otto-von-Guericke-University（奥托·冯·格里克大学）；Institute for Artificial Intelligence, University of Stuttgart（斯图加特大学人工智能研究所）；Faculty of Computer Science, UniVie Doctoral School Computer Science, University of Vienna（维也纳大学计算机科学系）；University of Southampton（南安普顿大学）
Visual Anomaly Synthesis for Model Selection in Data Scarcity
Digital Factory Vorarlberg GmbH（福拉尔贝格数字工厂有限公司）；illwerke vkw AG（伊尔沃克VKW股份公司）；University of Salzburg（萨尔茨堡大学）；Vorarlberg University of Applied Sciences（福拉尔贝格应用科学大学）
MVTec Software GmbH（MVTec软件有限公司）；Technical University of Munich（慕尼黑工业大学）；Karsruhe Institute of Technology（卡尔斯鲁厄理工学院）
We introduce the first comprehensive 3D dataset for the task of unsupervised anomaly detection and localization. It is inspired by real-world visual inspection scenarios in which a model has to detect various types of defects on manufactured products, even if it is trained only on anomaly-free data. There are defects that manifest themselves as anomalies in the geometric structure of an object. These cause significant deviations in a 3D representation of the data. We employed a high-resolution industrial 3D sensor to acquire depth scans of 10 different object categories. For all object categor…
Artificial Intelligence Research Institute (AIRI)（人工智能研究院（AIRI））；Hevel LLC（Hevel有限责任公司）；Saint Petersburg Electrotechnical University "LETI"（圣彼得堡国立电工大学“LETI”）；Unigreen Energy LLC（Unigreen能源有限责任公司）；HSE University（高等经济大学）；Sber AI Lab（俄罗斯联邦储蓄银行人工智能实验室）
Efficient defect detection in solar cell manufacturing is crucial for stable green energy technology manufacturing. This paper presents a deep-learning-based automatic detection model SeMaCNN for classification and semantic segmentation of electroluminescent images for solar cell quality evaluation and anomalies detection. The core of the model is an anomaly detection algorithm based on Mahalanobis distance that can be trained in a semi-supervised manner on imbalanced data with small number of digital electroluminescence images with relevant defects. This is particularly valuable for prompt mo…
