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A deep learning-based recognition for dangerous objects imaged in X-ray security inspection device.
- Source :
-
Journal of X-Ray Science & Technology . 2023, Vol. 31 Issue 1, p13-26. 14p. - Publication Year :
- 2023
-
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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08953996
- Volume :
- 31
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Journal of X-Ray Science & Technology
- Publication Type :
- Academic Journal
- Accession number :
- 161763069
- Full Text :
- https://doi.org/10.3233/XST-221210