Supervised Anomaly Detection for Complex Industrial Images

• URL: http://arxiv.org/abs/2405.04953v2
• Date: Sat, 11 May 2024 11:39:20 GMT
• Title: Supervised Anomaly Detection for Complex Industrial Images
• Authors: Aimira Baitieva, David Hurych, Victor Besnier, Olivier Bernard,
• Abstract summary: We present a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects. We also introduce (2)-based Anomaly Detector (SegAD) SegAD uses anomaly maps as well as segmentation maps to compute local statistics. Our SegAD state-of-the-art performance on both VAD and the VisA dataset (+0.4% AUROC)
• Score: 4.890533180388991
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automating visual inspection in industrial production lines is essential for increasing product quality across various industries. Anomaly detection (AD) methods serve as robust tools for this purpose. However, existing public datasets primarily consist of images without anomalies, limiting the practical application of AD methods in production settings. To address this challenge, we present (1) the Valeo Anomaly Dataset (VAD), a novel real-world industrial dataset comprising 5000 images, including 2000 instances of challenging real defects across more than 20 subclasses. Acknowledging that traditional AD methods struggle with this dataset, we introduce (2) Segmentation-based Anomaly Detector (SegAD). First, SegAD leverages anomaly maps as well as segmentation maps to compute local statistics. Next, SegAD uses these statistics and an optional supervised classifier score as input features for a Boosted Random Forest (BRF) classifier, yielding the final anomaly score. Our SegAD achieves state-of-the-art performance on both VAD (+2.1% AUROC) and the VisA dataset (+0.4% AUROC). The code and the models are publicly available.

Related papers

• DailyDVS-200: A Comprehensive Benchmark Dataset for Event-Based Action Recognition [51.96660522869841]
  DailyDVS-200 is a benchmark dataset tailored for the event-based action recognition community. It covers 200 action categories across real-world scenarios, recorded by 47 participants, and comprises more than 22,000 event sequences. DailyDVS-200 is annotated with 14 attributes, ensuring a detailed characterization of the recorded actions.
  arXiv  Detail & Related papers  (2024-07-06T15:25:10Z)
• ToCoAD: Two-Stage Contrastive Learning for Industrial Anomaly Detection [10.241033980055695]
  This paper presents a two-stage training strategy, called textbfToCoAD. In the first stage, a discriminative network is trained by using synthetic anomalies in a self-supervised learning manner. This network is then utilized in the second stage to provide a negative feature guide, aiding in the training of the feature extractor through bootstrap contrastive learning.
  arXiv  Detail & Related papers  (2024-07-01T14:19:36Z)
• Learning Feature Inversion for Multi-class Anomaly Detection under General-purpose COCO-AD Benchmark [101.23684938489413]
  Anomaly detection (AD) is often focused on detecting anomalies for industrial quality inspection and medical lesion examination. This work first constructs a large-scale and general-purpose COCO-AD dataset by extending COCO to the AD field. Inspired by the metrics in the segmentation field, we propose several more practical threshold-dependent AD-specific metrics.
  arXiv  Detail & Related papers  (2024-04-16T17:38:26Z)
• Test Time Training for Industrial Anomaly Segmentation [15.973768095014906]
  Anomaly Detection and Ranging (AD&S) is crucial for industrial quality control. This paper proposes a test time training strategy to improve the segmentation performance. We demonstrate the effectiveness of our approach over baselines through extensive experimentation and evaluation on MVTec AD and MVTec 3D-AD.
  arXiv  Detail & Related papers  (2024-04-04T18:31:24Z)
• Real-IAD: A Real-World Multi-View Dataset for Benchmarking Versatile Industrial Anomaly Detection [46.495442380849894]
  We propose a large-scale, Real-world, and multi-view Industrial Anomaly Detection dataset, named Real-IAD. It contains 150K high-resolution images of 30 different objects, an order of magnitude larger than existing datasets. To make the dataset closer to real application scenarios, we adopted a multi-view shooting method and proposed sample-level evaluation metrics.
  arXiv  Detail & Related papers  (2024-03-19T09:44:41Z)
• 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)
• Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction [2.921945366485149]
  Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. Existing IAD datasets focus on the diversity of data categories. We propose the Aero-engine Blade Anomaly Detection (AeBAD) dataset, consisting of two sub-datasets.
  arXiv  Detail & Related papers  (2023-04-05T04:07:54Z)
• Self-Trained One-class Classification for Unsupervised Anomaly Detection [56.35424872736276]
  Anomaly detection (AD) has various applications across domains, from manufacturing to healthcare. In this work, we focus on unsupervised AD problems whose entire training data are unlabeled and may contain both normal and anomalous samples. To tackle this problem, we build a robust one-class classification framework via data refinement. We show that our method outperforms state-of-the-art one-class classification method by 6.3 AUC and 12.5 average precision.
  arXiv  Detail & Related papers  (2021-06-11T01:36:08Z)
• Anomaly Detection Based on Selection and Weighting in Latent Space [73.01328671569759]
  We propose a novel selection-and-weighting-based anomaly detection framework called SWAD. Experiments on both benchmark and real-world datasets have shown the effectiveness and superiority of SWAD.
  arXiv  Detail & Related papers  (2021-03-08T10:56:38Z)

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.