MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection

• URL: http://arxiv.org/abs/2211.13968v2
• Date: Mon, 28 Nov 2022 09:22:02 GMT
• Title: MIAD: A Maintenance Inspection Dataset for Unsupervised Anomaly Detection
• Authors: Tianpeng Bao, Jiadong Chen, Wei Li, Xiang Wang, Jingjing Fei, Liwei Wu, Rui Zhao, Ye Zheng
• Abstract summary: Maintenance Inspection Anomaly Detection dataset contains more than 100K high-resolution color images. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth.
• Score: 20.74058429884136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Visual anomaly detection plays a crucial role in not only manufacturing inspection to find defects of products during manufacturing processes, but also maintenance inspection to keep equipment in optimum working condition particularly outdoors. Due to the scarcity of the defective samples, unsupervised anomaly detection has attracted great attention in recent years. However, existing datasets for unsupervised anomaly detection are biased towards manufacturing inspection, not considering maintenance inspection which is usually conducted under outdoor uncontrolled environment such as varying camera viewpoints, messy background and degradation of object surface after long-term working. We focus on outdoor maintenance inspection and contribute a comprehensive Maintenance Inspection Anomaly Detection (MIAD) dataset which contains more than 100K high-resolution color images in various outdoor industrial scenarios. This dataset is generated by a 3D graphics software and covers both surface and logical anomalies with pixel-precise ground truth. Extensive evaluations of representative algorithms for unsupervised anomaly detection are conducted, and we expect MIAD and corresponding experimental results can inspire research community in outdoor unsupervised anomaly detection tasks. Worthwhile and related future work can be spawned from our new dataset.

Related papers

• AnomalousPatchCore: Exploring the Use of Anomalous Samples in Industrial Anomaly Detection [2.2742404315918927]
  Visual inspection, or industrial anomaly detection, is one of the most common quality control types in manufacturing. Most anomaly detection methods still utilize knowledge only from normal samples, failing to leverage the information from the frequently available anomalous samples. We propose the new anomaly detection system AnomalousPatchCore( APC) based on a feature extractor fine-tuned with normal and anomalous in-domain samples.
  arXiv  Detail & Related papers  (2024-08-27T14:51:34Z)
• GeneralAD: Anomaly Detection Across Domains by Attending to Distorted Features [68.14842693208465]
  GeneralAD is an anomaly detection framework designed to operate in semantic, near-distribution, and industrial settings. We propose a novel self-supervised anomaly generation module that employs straightforward operations like noise addition and shuffling to patch features. We extensively evaluated our approach on ten datasets, achieving state-of-the-art results in six and on-par performance in the remaining.
  arXiv  Detail & Related papers  (2024-07-17T09:27:41Z)
• A Study on Unsupervised Anomaly Detection and Defect Localization using Generative Model in Ultrasonic Non-Destructive Testing [0.0]
  Deterioration of artificial materials used in structures has become a serious social issue. Laser ultrasonic visualization testing (LUVT) allows the visualization of ultrasonic propagation. We propose a method for automated LUVT inspection using an anomaly detection approach.
  arXiv  Detail & Related papers  (2024-05-26T14:14:35Z)
• Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
  We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets. We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
  arXiv  Detail & Related papers  (2024-01-06T07:30:41Z)
• Generating and Reweighting Dense Contrastive Patterns for Unsupervised Anomaly Detection [59.34318192698142]
  We introduce a prior-less anomaly generation paradigm and develop an innovative unsupervised anomaly detection framework named GRAD. PatchDiff effectively expose various types of anomaly patterns. experiments on both MVTec AD and MVTec LOCO datasets also support the aforementioned observation.
  arXiv  Detail & Related papers  (2023-12-26T07:08:06Z)
• PKU-GoodsAD: A Supermarket Goods Dataset for Unsupervised Anomaly Detection and Segmentation [6.950686169363205]
  This dataset contains 6124 high-resolution images of 484 different appearance goods divided into 6 categories. It follows the unsupervised setting and only normal (defect-free) images are used for training. We also conduct a thorough evaluation of current state-of-the-art unsupervised anomaly detection methods.
  arXiv  Detail & Related papers  (2023-07-11T01:17:00Z)
• Towards Meaningful Anomaly Detection: The Effect of Counterfactual Explanations on the Investigation of Anomalies in Multivariate Time Series [0.0]
  Among the anomalies detected may be events that are rare, e.g., a planned shutdown of a machine, but are not the actual event of interest. We propose to support this anomaly investigation by providing explanations of anomaly detection. We conduct a behavioral experiment using records of taxi rides in New York City as a testbed.
  arXiv  Detail & Related papers  (2023-02-07T07:27:26Z)
• Deep Learning for Time Series Anomaly Detection: A Survey [53.83593870825628]
  Time series anomaly detection has applications in a wide range of research fields and applications, including manufacturing and healthcare. The large size and complex patterns of time series have led researchers to develop specialised deep learning models for detecting anomalous patterns. This survey focuses on providing structured and comprehensive state-of-the-art time series anomaly detection models through the use of deep learning.
  arXiv  Detail & Related papers  (2022-11-09T22:40:22Z)
• An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots [76.36017224414523]
  We consider the problem of building visual anomaly detection systems for mobile robots. Standard anomaly detection models are trained using large datasets composed only of non-anomalous data. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model.
  arXiv  Detail & Related papers  (2022-09-20T15:18:13Z)
• Catching Both Gray and Black Swans: Open-set Supervised Anomaly Detection [90.32910087103744]
  A few labeled anomaly examples are often available in many real-world applications. These anomaly examples provide valuable knowledge about the application-specific abnormality. Those anomalies seen during training often do not illustrate every possible class of anomaly. This paper tackles open-set supervised anomaly detection.
  arXiv  Detail & Related papers  (2022-03-28T05:21:37Z)
• The MVTec 3D-AD Dataset for Unsupervised 3D Anomaly Detection and Localization [17.437967037670813]
  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.
  arXiv  Detail & Related papers  (2021-12-16T17:35:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.