On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery

Wang, Q.; Bhowmik, N.; Breckon, T.P.

Abstract

X-ray Computed Tomography (CT) based 3D imaging is widely used in airports for aviation security screening whilst prior work on prohibited item detection focuses primarily on 2D X-ray imagery. In this paper, we aim to evaluate the possibility of extending the automatic prohibited item detection from 2D X-ray imagery to volumetric 3D CT baggage security screening imagery. To these ends, we take advantage of 3D Convolutional Neural Networks (CNN) and popular object detection frameworks such as RetinaNet and Faster R-CNN in our work. As the first attempt to use 3D CNN for volumetric 3D CT baggage security screening, we first evaluate different CNN architectures on the classification of isolated prohibited item volumes and compare against traditional methods which use hand-crafted features. Subsequently, we evaluate object detection performance of different architectures on volumetric 3D CT baggage images. The results of our experiments on Bottle and Handgun datasets demonstrate that 3D CNN models can achieve comparable performance (~ 98% true positive rate and ~1.5% false positive rate) to traditional methods but require significantly less time for inference (0.014s per volume). Furthermore, the extended 3D object detection models achieve promising performance in detecting prohibited items within volumetric 3D CT baggage imagery with ~76% mAP for bottles and ~88% mAP for handguns, which shows both the challenge and promise of such threat detection within 3D CT X-ray security imagery.

Citation

Wang, Q., Bhowmik, N., & Breckon, T. (2020, July). On the Evaluation of Prohibited Item Classification and Detection in Volumetric 3D Computed Tomography Baggage Security Screening Imagery. Presented at International Joint Conference on Neural Networks, Glasgow, Scotland

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International Joint Conference on Neural Networks
• Start Date: Jul 19, 2020
• End Date: Jul 24, 2020
• Acceptance Date: Mar 20, 2020
• Online Publication Date: Sep 28, 2020
• Publication Date: Sep 28, 2020
• Deposit Date: Apr 21, 2020
• Publicly Available Date: Nov 27, 2020
• Pages: 1-8
• Series ISSN: 2161-4407
• Book Title: Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN)
• DOI: https://doi.org/10.1109/ijcnn48605.2020.9207389
• Public URL: https://durham-repository.worktribe.com/output/1142601