Wang, Q. and Bhowmik, N. and Breckon, T.P. (2020) 'On the evaluation of prohibited item classification and detection in volumetric 3D computed tomography baggage security screening imagery.', in Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN). Piscataway, NJ: IEEE, pp. 1-8.
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.
|Item Type:||Book chapter|
|Full text:||(AM) Accepted Manuscript|
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|Publisher Web site:||https://doi.org/10.1109/IJCNN48605.2020.9207389|
|Publisher statement:||© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.|
|Date accepted:||20 March 2020|
|Date deposited:||22 April 2020|
|Date of first online publication:||28 September 2020|
|Date first made open access:||27 November 2020|
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