An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots

• URL: http://arxiv.org/abs/2209.09786v1
• Date: Tue, 20 Sep 2022 15:18:13 GMT
• Title: An Outlier Exposure Approach to Improve Visual Anomaly Detection Performance for Mobile Robots
• Authors: Dario Mantegazza, Alessandro Giusti, Luca Maria Gambardella and J\'er\^ome Guzzi
• Abstract summary: 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.
• Score: 76.36017224414523
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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. However, in robotics applications, it is often the case that (potentially very few) examples of anomalies are available. We tackle the problem of exploiting these data to improve the performance of a Real-NVP anomaly detection model, by minimizing, jointly with the Real-NVP loss, an auxiliary outlier exposure margin loss. We perform quantitative experiments on a novel dataset (which we publish as supplementary material) designed for anomaly detection in an indoor patrolling scenario. On a disjoint test set, our approach outperforms alternatives and shows that exposing even a small number of anomalous frames yields significant performance improvements.

Related papers

• CableInspect-AD: An Expert-Annotated Anomaly Detection Dataset [14.246172794156987]
  $textitCableInspect-AD$ is a high-quality dataset created and annotated by domain experts from Hydro-Qu'ebec, a Canadian public utility. This dataset includes high-resolution images with challenging real-world anomalies, covering defects with varying severity levels. We present a comprehensive evaluation protocol based on cross-validation to assess models' performances.
  arXiv  Detail & Related papers  (2024-09-30T14:50:13Z)
• CUT: A Controllable, Universal, and Training-Free Visual Anomaly Generation Framework [11.609545429511595]
  We propose CUT: a Controllable, Universal and Training-free visual anomaly generation framework. We achieve controllable and realistic anomaly generation universally across both unseen data and novel anomaly types. By training the VLAD model with our generated anomalous samples, we achieve state-of-the-art performance on several benchmark anomaly detection tasks.
  arXiv  Detail & Related papers  (2024-06-03T07:58:09Z)
• 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)
• AnomalyDiffusion: Few-Shot Anomaly Image Generation with Diffusion Model [59.08735812631131]
  Anomaly inspection plays an important role in industrial manufacture. Existing anomaly inspection methods are limited in their performance due to insufficient anomaly data. We propose AnomalyDiffusion, a novel diffusion-based few-shot anomaly generation model.
  arXiv  Detail & Related papers  (2023-12-10T05:13:40Z)
• Open-Vocabulary Video Anomaly Detection [57.552523669351636]
  Video anomaly detection (VAD) with weak supervision has achieved remarkable performance in utilizing video-level labels to discriminate whether a video frame is normal or abnormal. Recent studies attempt to tackle a more realistic setting, open-set VAD, which aims to detect unseen anomalies given seen anomalies and normal videos. This paper takes a step further and explores open-vocabulary video anomaly detection (OVVAD), in which we aim to leverage pre-trained large models to detect and categorize seen and unseen anomalies.
  arXiv  Detail & Related papers  (2023-11-13T02:54:17Z)
• Sensing Anomalies as Potential Hazards: Datasets and Benchmarks [43.55994393060723]
  We consider the problem of detecting, in the visual sensing data stream of an autonomous mobile robot, semantic patterns that are unusual. We contribute three novel image-based datasets acquired in robot exploration scenarios. We study the performance of an anomaly detection approach based on autoencoders operating at different scales.
  arXiv  Detail & Related papers  (2021-10-27T18:47:06Z)
• DAE : Discriminatory Auto-Encoder for multivariate time-series anomaly detection in air transportation [68.8204255655161]
  We propose a novel anomaly detection model called Discriminatory Auto-Encoder (DAE) It uses the baseline of a regular LSTM-based auto-encoder but with several decoders, each getting data of a specific flight phase. Results show that the DAE achieves better results in both accuracy and speed of detection.
  arXiv  Detail & Related papers  (2021-09-08T14:07:55Z)
• Explainable Deep Few-shot Anomaly Detection with Deviation Networks [123.46611927225963]
  We introduce a novel weakly-supervised anomaly detection framework to train detection models. The proposed approach learns discriminative normality by leveraging the labeled anomalies and a prior probability. Our model is substantially more sample-efficient and robust, and performs significantly better than state-of-the-art competing methods in both closed-set and open-set settings.
  arXiv  Detail & Related papers  (2021-08-01T14:33:17Z)
• Out-Of-Bag Anomaly Detection [0.9449650062296822]
  Data anomalies are ubiquitous in real world datasets, and can have an adverse impact on machine learning (ML) systems. We propose a novel model-based anomaly detection method, that we call Out-of-Bag anomaly detection. We show our method can improve the accuracy and reliability of an ML system as data pre-processing step via a case study on home valuation.
  arXiv  Detail & Related papers  (2020-09-20T06:01:52Z)

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.