PAD-F: Prior-Aware Debiasing Framework for Long-Tailed X-ray Prohibited Item Detection

• URL: http://arxiv.org/abs/2411.18078v4
• Date: Wed, 13 Aug 2025 04:23:42 GMT
• Title: PAD-F: Prior-Aware Debiasing Framework for Long-Tailed X-ray Prohibited Item Detection
• Authors: Haoyu Wang, Renshuai Tao, Wei Wang, Yunchao Wei,
• Abstract summary: The distribution of object classes in real-world prohibited item detection scenarios often exhibits a distinct long-tailed distribution.<n>We introduce the Prior-Aware Debiasing Framework (PAD-F), a novel approach that employs a two-pronged strategy.<n>PAD-F significantly boosts the performance of multiple popular detectors.
• Score: 56.25222232778367
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Detecting prohibited items in X-ray security imagery is a challenging yet crucial task. With the rapid advancement of deep learning, object detection algorithms have been widely applied in this area. However, the distribution of object classes in real-world prohibited item detection scenarios often exhibits a distinct long-tailed distribution. Due to the unique principles of X-ray imaging, conventional methods for long-tailed object detection are often ineffective in this domain. To tackle these challenges, we introduce the Prior-Aware Debiasing Framework (PAD-F), a novel approach that employs a two-pronged strategy leveraging both material and co-occurrence priors. At the data level, our Explicit Material-Aware Augmentation (EMAA) component generates numerous challenging training samples for tail classes. It achieves this through a placement strategy guided by material-specific absorption rates and a gradient-based Poisson blending technique. At the feature level, the Implicit Co-occurrence Aggregator (ICA) acts as a plug-in module that enhances features for ambiguous objects by implicitly learning and aggregating statistical co-occurrence relationships within the image. Extensive experiments on the HiXray and PIDray datasets demonstrate that PAD-F significantly boosts the performance of multiple popular detectors. It achieves an absolute improvement of up to +17.2% in AP50 for tail classes and comprehensively outperforms existing state-of-the-art methods. Our work provides an effective and versatile solution to the critical problem of long-tailed detection in X-ray security.

Related papers

• X-ray Insights Unleashed: Pioneering the Enhancement of Multi-Label Long-Tail Data [86.52299247918637]
  Long-tailed pulmonary anomalies in chest radiography present formidable diagnostic challenges.<n>Despite the recent strides in diffusion-based methods for enhancing the representation of tailed lesions, the paucity of rare lesion exemplars curtails the generative capabilities of these approaches.<n>We propose a novel data synthesis pipeline designed to augment tail lesions utilizing a copious supply of conventional normal X-rays.
  arXiv  Detail & Related papers  (2025-12-24T06:14:55Z)
• 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)
• Exploring Active Learning for Semiconductor Defect Segmentation [20.72106200701627]
  In this work, we explore active learning (AL) as a potential solution to alleviate the annotation burden.<n>We identify two unique challenges when applying AL on semiconductor XRM scans: large domain shift and severe class-imbalance.<n>To address these challenges, we propose to perform contrastive pretraining on the unlabelled data.<n>We evaluate our method on a semiconductor dataset that is compiled from XRM scans of high bandwidth memory structures composed of logic and memory dies.
  arXiv  Detail & Related papers  (2025-07-23T09:44:11Z)
• Self-Supervised Multiview Xray Matching [4.033064933995391]
  Current methods often struggle to establish robust correspondences between different X-ray views.<n>We present a novel self-supervised pipeline that eliminates the need for manual annotation.<n>Our approach incorporates a transformer-based training phase to accurately predict correspondences across two or more X-ray views.
  arXiv  Detail & Related papers  (2025-06-30T21:56:14Z)
• 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. We propose RAXO, a framework that repurposes off-the-shelf RGB OvOD detectors for robust X-ray detection. RAXO builds high-quality X-ray class descriptors using a dual-source retrieval strategy.
  arXiv  Detail & Related papers  (2025-03-21T11:54:16Z)
• AdverX-Ray: Ensuring X-Ray Integrity Through Frequency-Sensitive Adversarial VAEs [7.0477485974331895]
  AdverX-Ray serves as an image-quality assessment layer. It is trained on patches from X-ray images of specific machine models. It can evaluate whether a scan matches the training distribution, or if a scan from the same machine is captured under different settings.
  arXiv  Detail & Related papers  (2025-02-23T15:32:40Z)
• BGM: Background Mixup for X-ray Prohibited Items Detection [75.58709178012502]
  This paper introduces a novel data augmentation approach tailored for prohibited item detection, leveraging unique characteristics inherent to X-ray imagery. Our method is motivated by observations of physical properties including: 1) X-ray Transmission Imagery: Unlike reflected light images, transmitted X-ray pixels represent composite information from multiple materials along the imaging path. We propose a simple yet effective X-ray image augmentation technique, Background Mixup (BGM), for prohibited item detection in security screening contexts.
  arXiv  Detail & Related papers  (2024-11-30T12:26:55Z)
• Dual-view X-ray Detection: Can AI Detect Prohibited Items from Dual-view X-ray Images like Humans? [78.26435264182763]
  We introduce the Large-scale Dual-view X-ray (LDXray), which consists of 353,646 instances across 12 categories.<n>To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet)<n>Experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance.
  arXiv  Detail & Related papers  (2024-11-27T06:36:20Z)
• Open-Set Deepfake Detection: A Parameter-Efficient Adaptation Method with Forgery Style Mixture [81.93945602120453]
  We introduce an approach that is both general and parameter-efficient for face forgery detection.<n>We design a forgery-style mixture formulation that augments the diversity of forgery source domains.<n>We show that the designed model achieves state-of-the-art generalizability with significantly reduced trainable parameters.
  arXiv  Detail & Related papers  (2024-08-23T01:53:36Z)
• 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)
• Model X-ray:Detecting Backdoored Models via Decision Boundary [62.675297418960355]
  Backdoor attacks pose a significant security vulnerability for deep neural networks (DNNs) We propose Model X-ray, a novel backdoor detection approach based on the analysis of illustrated two-dimensional (2D) decision boundaries. Our approach includes two strategies focused on the decision areas dominated by clean samples and the concentration of label distribution.
  arXiv  Detail & Related papers  (2024-02-27T12:42:07Z)
• Spatial-Frequency Discriminability for Revealing Adversarial Perturbations [53.279716307171604]
  Vulnerability of deep neural networks to adversarial perturbations has been widely perceived in the computer vision community. Current algorithms typically detect adversarial patterns through discriminative decomposition for natural and adversarial data. We propose a discriminative detector relying on a spatial-frequency Krawtchouk decomposition.
  arXiv  Detail & Related papers  (2023-05-18T10:18:59Z)
• Illicit item detection in X-ray images for security applications [7.519872646378835]
  Automated detection of contraband items in X-ray images can significantly increase public safety. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task. This paper proposes a two-fold improvement of such algorithms for the X-ray analysis domain.
  arXiv  Detail & Related papers  (2023-05-03T07:28:05Z)
• PIDray: A Large-scale X-ray Benchmark for Real-World Prohibited Item Detection [21.055813365091662]
  We present a large-scale dataset, named PIDray, which covers various cases in real-world scenarios for prohibited item detection. In specific, PIDray collects 124,486 X-ray images for $12$ categories of prohibited items. We propose a general divide-and-conquer pipeline to develop baseline algorithms on PIDray.
  arXiv  Detail & Related papers  (2022-11-19T18:31:34Z)
• Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery [14.785070524184649]
  The performance is evaluated over a dataset of cluttered X-ray baggage security imagery. The proposed joint sub-component level segmentation and classification approach achieve 99% true positive and 5% false positive for anomaly detection task.
  arXiv  Detail & Related papers  (2022-10-29T00:44:50Z)
• 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)
• On the impact of using X-ray energy response imagery for object detection via Convolutional Neural Networks [17.639472693362926]
  We study the impact of variant X-ray imagery, i.e. X-ray energy response (high, low) and effective-z compared to geometries. We evaluate CNN architectures to explore the transferability of models trained with such 'raw' variant imagery.
  arXiv  Detail & Related papers  (2021-08-27T21:28:28Z)
• Towards Real-world X-ray Security Inspection: A High-Quality Benchmark and Lateral Inhibition Module for Prohibited Items Detection [37.66855218659698]
  We first present a High-quality X-ray (HiXray) security inspection image dataset, which contains 102,928 common prohibited items of 8 categories. For accurate prohibited item detection, we propose the Lateral Inhibition Module (LIM) inspired by the fact that humans recognize these items by ignoring irrelevant information.
  arXiv  Detail & Related papers  (2021-08-23T03:59:23Z)
• Towards Real-World Prohibited Item Detection: A Large-Scale X-ray Benchmark [53.9819155669618]
  This paper presents a large-scale dataset, named as PIDray, which covers various cases in real-world scenarios for prohibited item detection. With an intensive amount of effort, our dataset contains $12$ categories of prohibited items in $47,677$ X-ray images with high-quality annotated segmentation masks and bounding boxes. The proposed method performs favorably against the state-of-the-art methods, especially for detecting the deliberately hidden items.
  arXiv  Detail & Related papers  (2021-08-16T11:14:16Z)
• Cross-Modal Contrastive Learning for Abnormality Classification and Localization in Chest X-rays with Radiomics using a Feedback Loop [63.81818077092879]
  We propose an end-to-end semi-supervised cross-modal contrastive learning framework for medical images. We first apply an image encoder to classify the chest X-rays and to generate the image features. The radiomic features are then passed through another dedicated encoder to act as the positive sample for the image features generated from the same chest X-ray.
  arXiv  Detail & Related papers  (2021-04-11T09:16:29Z)
• Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images [35.35752470993847]
  Security inspection is X-ray scanning for personal belongings in suitcases. Traditional CNN-based models trained through common image recognition datasets fail to achieve satisfactory performance in this scenario. We propose an over-sampling de-occlusion attention network (DOAM-O), which consists of a novel de-occlusion attention module and a new over-sampling training strategy.
  arXiv  Detail & Related papers  (2021-03-01T07:17:37Z)
• Occluded Prohibited Items Detection: an X-ray Security Inspection Benchmark and De-occlusion Attention Module [50.75589128518707]
  We contribute the first high-quality object detection dataset for security inspection, named OPIXray. OPIXray focused on the widely-occurred prohibited item "cutter", annotated manually by professional inspectors from the international airport. We propose the De-occlusion Attention Module (DOAM), a plug-and-play module that can be easily inserted into and thus promote most popular detectors.
  arXiv  Detail & Related papers  (2020-04-18T16:10:55Z)

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.