On the impact of using X-ray energy response imagery for object detection via Convolutional Neural Networks

• URL: http://arxiv.org/abs/2108.12505v1
• Date: Fri, 27 Aug 2021 21:28:28 GMT
• Title: On the impact of using X-ray energy response imagery for object detection via Convolutional Neural Networks
• Authors: Neelanjan Bhowmik, Yona Falinie A. Gaus, Toby P. Breckon
• Abstract summary: 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.
• Score: 17.639472693362926
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Automatic detection of prohibited items within complex and cluttered X-ray security imagery is essential to maintaining transport security, where prior work on automatic prohibited item detection focus primarily on pseudo-colour (rgb}) X-ray imagery. In this work we study the impact of variant X-ray imagery, i.e., X-ray energy response (high, low}) and effective-z compared to rgb, via the use of deep Convolutional Neural Networks (CNN) for the joint object detection and segmentation task posed within X-ray baggage security screening. We evaluate state-of-the-art CNN architectures (Mask R-CNN, YOLACT, CARAFE and Cascade Mask R-CNN) to explore the transferability of models trained with such 'raw' variant imagery between the varying X-ray security scanners that exhibits differing imaging geometries, image resolutions and material colour profiles. Overall, we observe maximal detection performance using CARAFE, attributable to training using combination of rgb, high, low, and effective-z X-ray imagery, obtaining 0.7 mean Average Precision (mAP) for a six class object detection problem. Our results also exhibit a remarkable degree of generalisation capability in terms of cross-scanner transferability (AP: 0.835/0.611) for a one class object detection problem by combining rgb, high, low, and effective-z imagery.

Related papers

• Fan-Beam CT Reconstruction for Unaligned Sparse-View X-ray Baggage Dataset [0.0]
  We present a calibration and reconstruction method using an unaligned sparse multi-view X-ray baggage dataset. Our approach integrates multi-spectral neural attenuation field reconstruction with Linear pushbroom (LPB) camera model pose optimization.
  arXiv  Detail & Related papers  (2024-12-04T05:16:54Z)
• 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)
• Illicit object detection in X-ray images using Vision Transformers [6.728794938150435]
  Illicit object detection is a critical task performed at various high-security locations. This study utilizes both Transformer and hybrid backbones, such as SWIN and NextViT, and detectors, such as DINO and RT-DETR.
  arXiv  Detail & Related papers  (2024-03-27T22:36:02Z)
• Artificial Intelligence for Automatic Detection and Classification Disease on the X-Ray Images [0.0]
  This work presents rapid detection of diseases in the lung using the efficient Deep learning pre-trained RepVGG algorithm. We are applying Artificial Intelligence technology for automatic highlighted detection of affected areas of people's lungs.
  arXiv  Detail & Related papers  (2022-11-14T03:51:12Z)
• Affinity Feature Strengthening for Accurate, Complete and Robust Vessel Segmentation [48.638327652506284]
  Vessel segmentation is crucial in many medical image applications, such as detecting coronary stenoses, retinal vessel diseases and brain aneurysms. We present a novel approach, the affinity feature strengthening network (AFN), which jointly models geometry and refines pixel-wise segmentation features using a contrast-insensitive, multiscale affinity approach.
  arXiv  Detail & Related papers  (2022-11-12T05:39:17Z)
• 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)
• 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)
• 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.