Illicit item detection in X-ray images for security applications

• URL: http://arxiv.org/abs/2305.01936v1
• Date: Wed, 3 May 2023 07:28:05 GMT
• Title: Illicit item detection in X-ray images for security applications
• Authors: Georgios Batsis, Ioannis Mademlis, Georgios Th. Papadopoulos
• Abstract summary: 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.
• Score: 7.519872646378835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Automated detection of contraband items in X-ray images can significantly increase public safety, by enhancing the productivity and alleviating the mental load of security officers in airports, subways, customs/post offices, etc. The large volume and high throughput of passengers, mailed parcels, etc., during rush hours make it a Big Data analysis task. Modern computer vision algorithms relying on Deep Neural Networks (DNNs) have proven capable of undertaking this task even under resource-constrained and embedded execution scenarios, e.g., as is the case with fast, single-stage, anchor-based object detectors. This paper proposes a two-fold improvement of such algorithms for the X-ray analysis domain, introducing two complementary novelties. Firstly, more efficient anchors are obtained by hierarchical clustering the sizes of the ground-truth training set bounding boxes; thus, the resulting anchors follow a natural hierarchy aligned with the semantic structure of the data. Secondly, the default Non-Maximum Suppression (NMS) algorithm at the end of the object detection pipeline is modified to better handle occluded object detection and to reduce the number of false predictions, by inserting the Efficient Intersection over Union (E-IoU) metric into the Weighted Cluster NMS method. E-IoU provides more discriminative geometrical correlations between the candidate bounding boxes/Regions-of-Interest (RoIs). The proposed method is implemented on a common single-stage object detector (YOLOv5) and its experimental evaluation on a relevant public dataset indicates significant accuracy gains over both the baseline and competing approaches. This highlights the potential of Big Data analysis in enhancing public safety.

Related papers

• Visual inspection for illicit items in X-ray images using Deep Learning [7.350725076596881]
  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.
  arXiv  Detail & Related papers  (2023-10-05T16:35:27Z)
• Small Object Detection via Coarse-to-fine Proposal Generation and Imitation Learning [52.06176253457522]
  We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning. CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
  arXiv  Detail & Related papers  (2023-08-18T13:13:09Z)
• 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)
• End-to-End Instance Edge Detection [29.650295133113183]
  Edge detection has long been an important problem in the field of computer vision. Previous works have explored category-agnostic or category-aware edge detection. In this paper, we explore edge detection in the context of object instances.
  arXiv  Detail & Related papers  (2022-04-06T15:32:21Z)
• MD-CSDNetwork: Multi-Domain Cross Stitched Network for Deepfake Detection [80.83725644958633]
  Current deepfake generation methods leave discriminative artifacts in the frequency spectrum of fake images and videos. We present a novel approach, termed as MD-CSDNetwork, for combining the features in the spatial and frequency domains to mine a shared discriminative representation.
  arXiv  Detail & Related papers  (2021-09-15T14:11:53Z)
• DAFNe: A One-Stage Anchor-Free Deep Model for Oriented Object Detection [16.21161769128316]
  We present DAFNe: A one-stage Anchor-Free deep Network for oriented object detection. As an anchor-free model, DAFNe reduces the prediction complexity by refraining from employing bounding box anchors. We introduce an orientation-aware generalization of the center-ness function for arbitrarily oriented bounding boxes to down-weight low-quality predictions.
  arXiv  Detail & Related papers  (2021-09-13T17:37:20Z)
• MRDet: A Multi-Head Network for Accurate Oriented Object Detection in Aerial Images [51.227489316673484]
  We propose an arbitrary-oriented region proposal network (AO-RPN) to generate oriented proposals transformed from horizontal anchors. To obtain accurate bounding boxes, we decouple the detection task into multiple subtasks and propose a multi-head network. Each head is specially designed to learn the features optimal for the corresponding task, which allows our network to detect objects accurately.
  arXiv  Detail & Related papers  (2020-12-24T06:36:48Z)
• Dense Label Encoding for Boundary Discontinuity Free Rotation Detection [69.75559390700887]
  This paper explores a relatively less-studied methodology based on classification. We propose new techniques to push its frontier in two aspects. Experiments and visual analysis on large-scale public datasets for aerial images show the effectiveness of our approach.
  arXiv  Detail & Related papers  (2020-11-19T05:42:02Z)
• Background Adaptive Faster R-CNN for Semi-Supervised Convolutional Object Detection of Threats in X-Ray Images [64.39996451133268]
  We present a semi-supervised approach for threat recognition which we call Background Adaptive Faster R-CNN. This approach is a training method for two-stage object detectors which uses Domain Adaptation methods from the field of deep learning. Two domain discriminators, one for discriminating object proposals and one for image features, are adversarially trained to prevent encoding domain-specific information. This can reduce threat detection false alarm rates by matching the statistics of extracted features from hand-collected backgrounds to real world data.
  arXiv  Detail & Related papers  (2020-10-02T21:05:13Z)
• Anchor-free Small-scale Multispectral Pedestrian Detection [88.7497134369344]
  We propose a method for effective and efficient multispectral fusion of the two modalities in an adapted single-stage anchor-free base architecture. We aim at learning pedestrian representations based on object center and scale rather than direct bounding box predictions. Results show our method's effectiveness in detecting small-scaled pedestrians.
  arXiv  Detail & Related papers  (2020-08-19T13:13:01Z)

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.