An aeronautic X-ray image security inspection network for rotation and occlusion. (13th June 2023)

• Record Type: Journal Article
• Authors: Su, Bingshan; An, Shiyong; Zhao, Xuezhuan; Chen, Jiguang; Li, Xiaoyu; He, Yuantao
• Is Part Of: International journal of computational science and engineering. Volume 26:Number 3(2023)
• Page Start: 337
• Page End: 348
• Publication Date: 2023-06-13
• Subjects: X-ray image detection -- de-rotation-and-occlusion -- deep learning -- ORB -- SSD; Computer science -- Mathematics -- Periodicals; Computer simulation -- Mathematical aspects -- Periodicals; Computational intelligence -- Periodicals; 004.015105
• Languages: English
• ISSNs: 1742-7185

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.