Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images

• URL: http://arxiv.org/abs/2103.00809v1
• Date: Mon, 1 Mar 2021 07:17:37 GMT
• Title: Over-sampling De-occlusion Attention Network for Prohibited Items Detection in Noisy X-ray Images
• Authors: Renshuai Tao, Yanlu Wei, Hainan Li, Aishan Liu, Yifu Ding, Haotong Qin and Xianglong Liu
• Abstract summary: 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.
• Score: 35.35752470993847
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Security inspection is X-ray scanning for personal belongings in suitcases, which is significantly important for the public security but highly time-consuming for human inspectors. Fortunately, deep learning has greatly promoted the development of computer vision, offering a possible way of automatic security inspection. However, items within a luggage are randomly overlapped resulting in noisy X-ray images with heavy occlusions. Thus, traditional CNN-based models trained through common image recognition datasets fail to achieve satisfactory performance in this scenario. To address these problems, we contribute the first high-quality prohibited X-ray object detection dataset named OPIXray, which contains 8885 X-ray images from 5 categories of the widely-occurred prohibited item ``cutters''. The images are gathered from an airport and these prohibited items are annotated manually by professional inspectors, which can be used as a benchmark for model training and further facilitate future research. To better improve occluded X-ray object detection, we further 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. Specifically, our de-occlusion module, namely DOAM, simultaneously leverages the different appearance information of the prohibited items; the over-sampling training strategy forces the model to put more emphasis on these hard samples consisting these items of high occlusion levels, which is more suitable for this scenario. We comprehensively evaluated DOAM-O on the OPIXray dataset, which proves that our model can stably improve the performance of the famous detection models such as SSD, YOLOv3, and FCOS, and outperform many extensively-used attention mechanisms.

Related papers

• 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)
• Understanding and Improving Training-Free AI-Generated Image Detections with Vision Foundation Models [68.90917438865078]
  Deepfake techniques for facial synthesis and editing pose serious risks for generative models. In this paper, we investigate how detection performance varies across model backbones, types, and datasets. We introduce Contrastive Blur, which enhances performance on facial images, and MINDER, which addresses noise type bias, balancing performance across domains.
  arXiv  Detail & Related papers  (2024-11-28T13:04:45Z)
• Dual-Level Boost Network for Long-Tail Prohibited Items Detection in X-ray Security Inspection [81.11400642272976]
  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.
  arXiv  Detail & Related papers  (2024-11-27T06:13:56Z)
• Temporal Fusion Based Mutli-scale Semantic Segmentation for Detecting Concealed Baggage Threats [12.895636885728852]
  No framework exists that utilizes temporal baggage X-ray imagery to effectively screen highly concealed objects. We present a novel temporal fusion driven multi-scale residual fashioned encoder-decoder that takes series of consecutive scans as input. The proposed framework outperforms its competitors on the GDXray dataset on various metrics.
  arXiv  Detail & Related papers  (2021-11-04T06:19:52Z)
• 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)
• 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.