Title: An aeronautic X-ray image security inspection network for rotation and occlusion

Authors: Bingshan Su; Shiyong An; Xuezhuan Zhao; Jiguang Chen; Xiaoyu Li; Yuantao He

Addresses: School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China ' National Key Laboratory of Fundamental Science on Synthetic Vision, Sichuan University, Chengdu 610065, China ' School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China ' School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China ' School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China ' School of Intelligent Engineering, Zhengzhou University of Aeronautics, Zhengzhou 450046, China

Abstract: Aviation security inspection needs lots of time and human labour. In this paper, we establish a new network for detecting prohibited objects in aeronautic security inspection X-ray images. Objects in the X-ray image often present rotated shapes and overlap heavily with each other. In order to solve the rotation and occlusion in X-ray image detection, we construct the de-rotation-and-occlusion module (DROM), an efficient module that can be embedded into most deep learning detectors. Our DROM leverages the edge, colour and oriented fast and rotated brief (ORB) features to generate an integrated feature map, while the ORB features could be extracted quickly and diminish the deviation produced by rotation effectively. Finally, we evaluate DROM on the OPIXRay dataset; compared with several latest approaches, the experimental results certify that our module promotes the performance of single shot multibox detector (SSD) and obtains higher accuracy, which proves the module's application value in practical security inspection.

Keywords: X-ray image detection; de-rotation-and-occlusion; deep learning; ORB; SSD.

DOI: 10.1504/IJCSE.2023.131504

International Journal of Computational Science and Engineering, 2023 Vol.26 No.3, pp.337 - 348

Received: 01 Nov 2021
Accepted: 27 Mar 2022
Published online: 15 Jun 2023 *

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