Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics

• URL: http://arxiv.org/abs/2310.17316v5
• Date: Fri, 19 Jul 2024 16:10:14 GMT
• Title: Defect Spectrum: A Granular Look of Large-Scale Defect Datasets with Rich Semantics
• Authors: Shuai Yang, Zhifei Chen, Pengguang Chen, Xi Fang, Yixun Liang, Shu Liu, Yingcong Chen,
• Abstract summary: We introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich semantic details, distinguishing multiple defect types within a single image. We also introduce Defect-Gen, a two-stage diffusion-based generator designed to create high-quality and diverse defective images.
• Score: 27.03052142039447
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Defect inspection is paramount within the closed-loop manufacturing system. However, existing datasets for defect inspection often lack precision and semantic granularity required for practical applications. In this paper, we introduce the Defect Spectrum, a comprehensive benchmark that offers precise, semantic-abundant, and large-scale annotations for a wide range of industrial defects. Building on four key industrial benchmarks, our dataset refines existing annotations and introduces rich semantic details, distinguishing multiple defect types within a single image. Furthermore, we introduce Defect-Gen, a two-stage diffusion-based generator designed to create high-quality and diverse defective images, even when working with limited datasets. The synthetic images generated by Defect-Gen significantly enhance the efficacy of defect inspection models. Overall, The Defect Spectrum dataset demonstrates its potential in defect inspection research, offering a solid platform for testing and refining advanced models.

Related papers

• Fine-grained Abnormality Prompt Learning for Zero-shot Anomaly Detection [88.34095233600719]
  FAPrompt is a novel framework designed to learn Fine-grained Abnormality Prompts for more accurate ZSAD. It substantially outperforms state-of-the-art methods by at least 3%-5% AUC/AP in both image- and pixel-level ZSAD tasks.
  arXiv  Detail & Related papers  (2024-10-14T08:41:31Z)
• Change-Aware Siamese Network for Surface Defects Segmentation under Complex Background [0.6407952035735353]
  We propose a change-aware Siamese network that solves the defect segmentation in a change detection framework. A novel multi-class balanced contrastive loss is introduced to guide the Transformer-based encoder. The difference presented by a distance map is then skip-connected to the change-aware decoder to assist in the location of both inter-class and out-of-class pixel-wise defects.
  arXiv  Detail & Related papers  (2024-09-01T02:48:11Z)
• SINDER: Repairing the Singular Defects of DINOv2 [61.98878352956125]
  Vision Transformer models trained on large-scale datasets often exhibit artifacts in the patch token they extract. We propose a novel fine-tuning smooth regularization that rectifies structural deficiencies using only a small dataset.
  arXiv  Detail & Related papers  (2024-07-23T20:34:23Z)
• 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)
• An Improved Anomaly Detection Model for Automated Inspection of Power Line Insulators [0.0]
  Inspection of insulators is important to ensure reliable operation of the power system. Deep learning is being increasingly exploited to automate the inspection process. This article proposes the use of anomaly detection along with object detection in a two-stage approach for incipient fault detection.
  arXiv  Detail & Related papers  (2023-11-14T11:36:20Z)
• 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)
• 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)
• Learning to Identify Drilling Defects in Turbine Blades with Single Stage Detectors [15.842163335920954]
  We propose a model based on Retina drilling defects in X-ray images of turbine blades. The application is challenging due to the image resolutions in which defects are very small and hardly captured by the commonly used anchor sizes. We validate the model with $3$-fold cross-validation, showing a very high accuracy in identifying images with defects.
  arXiv  Detail & Related papers  (2022-08-08T18:44:51Z)
• Reference-based Defect Detection Network [57.89399576743665]
  The first issue is the texture shift which means a trained defect detector model will be easily affected by unseen texture. The second issue is partial visual confusion which indicates that a partial defect box is visually similar with a complete box. We propose a Reference-based Defect Detection Network (RDDN) to tackle these two problems.
  arXiv  Detail & Related papers  (2021-08-10T05:44:23Z)
• Defect-GAN: High-Fidelity Defect Synthesis for Automated Defect Inspection [34.699695525216185]
  Defect-GAN is an automated defect synthesis network that generates realistic and diverse defect samples. It learns through defacement and restoration processes, where the defacement generates defects on normal surface images. It can also mimic variations of defects and offer flexible control over the locations and categories of the generated defects.
  arXiv  Detail & Related papers  (2021-03-28T15:53:34Z)
• Semi-Supervised StyleGAN for Disentanglement Learning [79.01988132442064]
  Current disentanglement methods face several inherent limitations. We design new architectures and loss functions based on StyleGAN for semi-supervised high-resolution disentanglement learning.
  arXiv  Detail & Related papers  (2020-03-06T22:54:46Z)

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.