CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection

• URL: http://arxiv.org/abs/2505.03412v1
• Date: Tue, 06 May 2025 10:38:24 GMT
• Title: CXR-AD: Component X-ray Image Dataset for Industrial Anomaly Detection
• Authors: Haoyu Bai, Jie Wang, Gaomin Li, Xuan Li, Xiaohu Zhang, Xia Yang,
• Abstract summary: We construct the first publicly accessible component X-ray anomaly detection dataset, comprising real-world X-ray images.<n>The dataset covers five industrial component categories, including 653 normal samples and 561 defect samples with precise pixel-level mask annotations.<n>To the best of our knowledge, CXR-AD represents the first publicly available X-ray dataset for component anomaly detection.
• Score: 11.43229492795515
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Internal defect detection constitutes a critical process in ensuring component quality, for which anomaly detection serves as an effective solution. However, existing anomaly detection datasets predominantly focus on surface defects in visible-light images, lacking publicly available X-ray datasets targeting internal defects in components. To address this gap, we construct the first publicly accessible component X-ray anomaly detection (CXR-AD) dataset, comprising real-world X-ray images. The dataset covers five industrial component categories, including 653 normal samples and 561 defect samples with precise pixel-level mask annotations. We systematically analyze the dataset characteristics and identify three major technical challenges: (1) strong coupling between complex internal structures and defect regions, (2) inherent low contrast and high noise interference in X-ray imaging, and (3) significant variations in defect scales and morphologies. To evaluate dataset complexity, we benchmark three state-of-the-art anomaly detection frameworks (feature-based, reconstruction-based, and zero-shot learning methods). Experimental results demonstrate a 29.78% average performance degradation on CXR-AD compared to MVTec AD, highlighting the limitations of current algorithms in handling internal defect detection tasks. To the best of our knowledge, CXR-AD represents the first publicly available X-ray dataset for component anomaly detection, providing a real-world industrial benchmark to advance algorithm development and enhance precision in internal defect inspection technologies.

Related papers

• Illicit object detection in X-ray imaging using deep learning techniques: A comparative evaluation [9.33554429903529]
  Automated X-ray inspection is crucial for efficient and unobtrusive security screening in various public settings.<n>Despite the large body of research in the field, reported experimental evaluations are often incomplete.<n>To shed light on the research landscape and facilitate further research, a systematic, detailed, and comparative evaluation of recent Deep Learning (DL)-based methods for X-ray object detection is conducted.
  arXiv  Detail & Related papers  (2025-07-23T13:47:33Z)
• Superpowering Open-Vocabulary Object Detectors for X-ray Vision [53.07098133237041]
  Open-vocabulary object detection (OvOD) is set to revolutionize security screening by enabling systems to recognize any item in X-ray scans.<n>We propose RAXO, a framework that repurposes off-the-shelf RGB OvOD detectors for robust X-ray detection.<n> RAXO builds high-quality X-ray class descriptors using a dual-source retrieval strategy.
  arXiv  Detail & Related papers  (2025-03-21T11:54:16Z)
• A Multimodal Fusion Framework for Bridge Defect Detection with Cross-Verification [0.0]
  This paper introduces a multimodal fusion framework for the detection and analysis of bridge defects.<n>It integrates Non-Destructive Evaluation (NDE) techniques with advanced image processing to enable precise structural assessment.
  arXiv  Detail & Related papers  (2024-12-23T20:33:34Z)
• Enhancing Prohibited Item Detection through X-ray-Specific Augmentation and Contextual Feature Integration [81.11400642272976]
  X-ray prohibited item detection faces challenges due to the long-tail distribution and unique characteristics of X-ray imaging.<n>Traditional data augmentation strategies, such as copy-paste and mixup, are ineffective at improving the detection of rare items.<n>We propose the X-ray Imaging-driven Detection Network (XIDNet) to address these challenges.
  arXiv  Detail & Related papers  (2024-11-27T06:13:56Z)
• Texture-AD: An Anomaly Detection Dataset and Benchmark for Real Algorithm Development [8.290951816115887]
  We present the Texture-AD benchmark based on representative texture-based anomaly detection. This dataset includes images of 15 different cloth, 14 semiconductor wafers and 10 metal plates. To our knowledge, Texture-AD is the first dataset to be devoted to evaluating industrial defect detection algorithms in the real world.
  arXiv  Detail & Related papers  (2024-09-10T09:44:38Z)
• Weakly Supervised Anomaly Detection for Chest X-Ray Image [6.506869371228188]
  We propose a weakly supervised anomaly detection framework for Chest X-Ray (CXR) examination. WSCXR firstly constructs sets of normal and anomaly image features respectively. It then refines the anomaly image features by eliminating normal region features through anomaly feature mining.
  arXiv  Detail & Related papers  (2023-11-16T07:53:34Z)
• K-Space-Aware Cross-Modality Score for Synthesized Neuroimage Quality Assessment [71.27193056354741]
  The problem of how to assess cross-modality medical image synthesis has been largely unexplored. We propose a new metric K-CROSS to spur progress on this challenging problem. K-CROSS uses a pre-trained multi-modality segmentation network to predict the lesion location.
  arXiv  Detail & Related papers  (2023-07-10T01:26:48Z)
• Defect detection and segmentation in X-Ray images of magnesium alloy castings using the Detectron2 framework [0.13764085113103217]
  New production techniques have made it possible to produce metal parts with more complex shapes, making the quality control process more difficult. X-Ray images has made this process much easier, allowing not only to detect superficial defects in a much simpler way, but also to detect welding or casting defects. The aim of this paper is to apply a deep learning system based on Detectron2, a state-of-the-art library applied to object detection and segmentation in images.
  arXiv  Detail & Related papers  (2022-02-28T16:53:09Z)
• Generative Residual Attention Network for Disease Detection [51.60842580044539]
  We present a novel approach for disease generation in X-rays using a conditional generative adversarial learning. We generate a corresponding radiology image in a target domain while preserving the identity of the patient. We then use the generated X-ray image in the target domain to augment our training to improve the detection performance.
  arXiv  Detail & Related papers  (2021-10-25T14:15:57Z)
• Fader Networks for domain adaptation on fMRI: ABIDE-II study [68.5481471934606]
  We use 3D convolutional autoencoders to build the domain irrelevant latent space image representation and demonstrate this method to outperform existing approaches on ABIDE data.
  arXiv  Detail & Related papers  (2020-10-14T16:50:50Z)
• Deep Mining External Imperfect Data for Chest X-ray Disease Screening [57.40329813850719]
  We argue that incorporating an external CXR dataset leads to imperfect training data, which raises the challenges. We formulate the multi-label disease classification problem as weighted independent binary tasks according to the categories. Our framework simultaneously models and tackles the domain and label discrepancies, enabling superior knowledge mining ability.
  arXiv  Detail & Related papers  (2020-06-06T06:48:40Z)

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.