VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON

• URL: http://arxiv.org/abs/2306.07890v2
• Date: Sun, 18 Jun 2023 01:11:04 GMT
• Title: VISION Datasets: A Benchmark for Vision-based InduStrial InspectiON
• Authors: Haoping Bai, Shancong Mou, Tatiana Likhomanenko, Ramazan Gokberk Cinbis, Oncel Tuzel, Ping Huang, Jiulong Shan, Jianjun Shi, Meng Cao
• Abstract summary: VISION datasets are diverse collection of 14 industrial inspection datasets. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios.
• Score: 28.511625423590605
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite progress in vision-based inspection algorithms, real-world industrial challenges -- specifically in data availability, quality, and complex production requirements -- often remain under-addressed. We introduce the VISION Datasets, a diverse collection of 14 industrial inspection datasets, uniquely poised to meet these challenges. Unlike previous datasets, VISION brings versatility to defect detection, offering annotation masks across all splits and catering to various detection methodologies. Our datasets also feature instance-segmentation annotation, enabling precise defect identification. With a total of 18k images encompassing 44 defect types, VISION strives to mirror a wide range of real-world production scenarios. By supporting two ongoing challenge competitions on the VISION Datasets, we hope to foster further advancements in vision-based industrial inspection.

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)
• A PRISMA Driven Systematic Review of Publicly Available Datasets for Benchmark and Model Developments for Industrial Defect Detection [0.0]
  A critical barrier to progress is the scarcity of comprehensive datasets featuring annotated defects. This systematic review, spanning from 2015 to 2023, identifies 15 publicly available datasets. The goal of this systematic review is to consolidate these datasets in a single location, providing researchers with a comprehensive reference.
  arXiv  Detail & Related papers  (2024-06-11T20:14:59Z)
• A Comprehensive Library for Benchmarking Multi-class Visual Anomaly Detection [52.228708947607636]
  This paper introduces a comprehensive visual anomaly detection benchmark, ADer, which is a modular framework for new methods. The benchmark includes multiple datasets from industrial and medical domains, implementing fifteen state-of-the-art methods and nine comprehensive metrics. We objectively reveal the strengths and weaknesses of different methods and provide insights into the challenges and future directions of multi-class visual anomaly detection.
  arXiv  Detail & Related papers  (2024-06-05T13:40:07Z)
• BEHAVIOR Vision Suite: Customizable Dataset Generation via Simulation [57.40024206484446]
  We introduce the BEHAVIOR Vision Suite (BVS), a set of tools and assets to generate fully customized synthetic data for systematic evaluation of computer vision models. BVS supports a large number of adjustable parameters at the scene level. We showcase three example application scenarios.
  arXiv  Detail & Related papers  (2024-05-15T17:57:56Z)
• Advancing Video Anomaly Detection: A Concise Review and a New Dataset [8.822253683273841]
  Video Anomaly Detection (VAD) finds widespread applications in security surveillance, traffic monitoring, industrial monitoring, and healthcare. Despite extensive research efforts, there remains a lack of concise reviews that provide insightful guidance for researchers. We present such a review, examining models and datasets from various perspectives.
  arXiv  Detail & Related papers  (2024-02-07T13:54:56Z)
• A Survey on Visual Anomaly Detection: Challenge, Approach, and Prospect [29.006716009327032]
  Visual Anomaly Detection (VAD) endeavors to pinpoint deviations from the concept of normality in visual data, widely applied across diverse domains, e.g., industrial defect inspection, and medical lesion detection. This survey comprehensively examines recent advancements in VAD by identifying three primary challenges: 1) scarcity of training data, 2) diversity of visual modalities, and 3) complexity of hierarchical anomalies.
  arXiv  Detail & Related papers  (2024-01-29T18:41:21Z)
• Wild Face Anti-Spoofing Challenge 2023: Benchmark and Results [73.98594459933008]
  Face anti-spoofing (FAS) is an essential mechanism for safeguarding the integrity of automated face recognition systems. This limitation can be attributed to the scarcity and lack of diversity in publicly available FAS datasets. We introduce the Wild Face Anti-Spoofing dataset, a large-scale, diverse FAS dataset collected in unconstrained settings.
  arXiv  Detail & Related papers  (2023-04-12T10:29:42Z)
• Universal Object Detection with Large Vision Model [79.06618136217142]
  This study focuses on the large-scale, multi-domain universal object detection problem. To address these challenges, we introduce our approach to label handling, hierarchy-aware design, and resource-efficient model training. Our method has demonstrated remarkable performance, securing a prestigious second-place ranking in the object detection track of the Robust Vision Challenge 2022.
  arXiv  Detail & Related papers  (2022-12-19T12:40:13Z)
• A Comprehensive Survey on Edge Data Integrity Verification: Fundamentals and Future Trends [43.174689394432804]
  We show current research status, open problems, and potentially promising insights for readers to further investigate this under-explored field. To thoroughly assess prior research efforts, we synthesize a universal criteria framework that an effective verification approach should satisfy. We highlight intriguing research challenges and possible directions for future work, along with a discussion on how forthcoming technology, e.g., machine learning and context-aware security, can augment security in EC.
  arXiv  Detail & Related papers  (2022-10-20T02:58:36Z)
• Uncertainty-Aware Multi-View Representation Learning [53.06828186507994]
  We devise a novel unsupervised multi-view learning approach, termed as Dynamic Uncertainty-Aware Networks (DUA-Nets) Guided by the uncertainty of data estimated from the generation perspective, intrinsic information from multiple views is integrated to obtain noise-free representations. Our model achieves superior performance in extensive experiments and shows the robustness to noisy data.
  arXiv  Detail & Related papers  (2022-01-15T07:16:20Z)
• A Variational Information Bottleneck Approach to Multi-Omics Data Integration [98.6475134630792]
  We propose a deep variational information bottleneck (IB) approach for incomplete multi-view observations. Our method applies the IB framework on marginal and joint representations of the observed views to focus on intra-view and inter-view interactions that are relevant for the target. Experiments on real-world datasets show that our method consistently achieves gain from data integration and outperforms state-of-the-art benchmarks.
  arXiv  Detail & Related papers  (2021-02-05T06:05:39Z)

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.