Dual-Level Boost Network for Long-Tail Prohibited Items Detection in X-ray Security Inspection

• URL: http://arxiv.org/abs/2411.18078v1
• Date: Wed, 27 Nov 2024 06:13:56 GMT
• Title: Dual-Level Boost Network for Long-Tail Prohibited Items Detection in X-ray Security Inspection
• Authors: Renshuai Tao, Haoyu Wang, Wei Wang, Yunchao Wei, Yao Zhao,
• Abstract summary: Long-tail distribution of prohibited items in X-ray security inspections poses a big challenge for detection models. We propose a Dual-level Boost Network (DBNet) specifically designed to overcome these challenges in X-ray security screening. Our approach introduces two key innovations: (1) a specific data augmentation strategy employing Poisson blending, inspired by the characteristics of X-ray images, to generate realistic synthetic instances of rare items which can effectively mitigate data imbalance; and (2) a context-aware feature enhancement module that captures the spatial and semantic interactions between objects and their surroundings, enhancing classification accuracy for underrepresented categories.
• Score: 81.11400642272976
• License:
• Abstract: The detection of prohibited items in X-ray security inspections is vital for ensuring public safety. However, the long-tail distribution of item categories, where certain prohibited items are far less common, poses a big challenge for detection models, as rare categories often lack sufficient training data. Existing methods struggle to classify these rare items accurately due to this imbalance. In this paper, we propose a Dual-level Boost Network (DBNet) specifically designed to overcome these challenges in X-ray security screening. Our approach introduces two key innovations: (1) a specific data augmentation strategy employing Poisson blending, inspired by the characteristics of X-ray images, to generate realistic synthetic instances of rare items which can effectively mitigate data imbalance; and (2) a context-aware feature enhancement module that captures the spatial and semantic interactions between objects and their surroundings, enhancing classification accuracy for underrepresented categories. Extensive experimental results demonstrate that DBNet improves detection performance for tail categories, outperforming sota methods in X-ray security inspection scenarios by a large margin 17.2%, thereby ensuring enhanced public safety.

Related papers

• 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. To emulate human intelligence in dual-view detection, we propose the Auxiliary-view Enhanced Network (AENet) Experiments on the LDXray dataset demonstrate that the dual-view mechanism significantly enhances detection performance.
  arXiv  Detail & Related papers  (2024-11-27T06:36:20Z)
• 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)
• 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)
• 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)
• 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.