Affiliations: [a] School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China
| [b] Beijing Hangxing Machinery Manufacturing Co., Ltd, Dongcheng, Beijing, China
| [c] Xi’an Key Laboratory of Advanced Control and Intelligent Process, Xi’an, Shaanxi, China
| [d] Automatic Sorting Technology Research Center, Xi’an University of Posts and Telecommunications, State Post Bureau of the People’s Republic of China, Xi’an, Shaanxi, China
| [e] School of Communications and Information Engineering, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi, China
Correspondence:
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Corresponding author: Shaojie Tang, School of Automation, Xi’an University of Posts and Telecommunications, Xi’an, Shaanxi 710121, China. E-mail: tangshaojie@xupt.edu.cn.
Abstract: Several limitations in algorithms and datasets in the field of X-ray security inspection result in the low accuracy of X-ray image inspection. In the literature, there have been rare studies proposed and datasets prepared for the topic of dangerous objects segmentation. In this work, we contribute a purely manual segmentation for labeling the existing X-ray security inspection dataset namely, SIXRay, with the pixel-level semantic information of dangerous objects. We also propose a composition method for X-ray security inspection images to effectively augment the positive samples. This composition method can quickly obtain the positive sample images using affine transformation and HSV features of X-ray images. Furthermore, to improve the recognition accuracy, especially for adjacent and overlapping dangerous objects, we propose to combine the target detection algorithm (i.e., the softer-non maximum suppression, Softer-NMS) with Mask RCNN, which is named as the Softer-Mask RCNN. Compared with the original model (i.e., Mask RCNN), the Softer-Mask RCNN improves by 3.4% in accuracy (mAP), and 6.2% with adding synthetic data. The study result indicates that our proposed method in this work can effectively improve the recognition performance of dangerous objects depicting in the X-ray security inspection images.
Keywords: Deep learning, data augmentation, mask RCNN, security inspection, object recognition
