Combination of deep learning with representation learning in X-ray prohibited item detection
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Published:2023-06-21
Issue:
Volume:11
Page:
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ISSN:2296-424X
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Container-title:Frontiers in Physics
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language:
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Short-container-title:Front. Phys.
Author:
Rao Jianghao,Qin Peng,Zhou Gaofan,Li Meihui,Zhang Jianlin,Bao Qiliang,Peng Zhenming
Abstract
During X-ray inspection detection, a detector converts the collected X-rays from objects into electrical signals, which are then transmitted to a computer for image processing and analysis. From the aspect of digital image processing, detection tasks mainly focus on data processing and transformation to identify valuable features, which make the algorithms more effective. The consistent requirement for speed and accuracy in X-ray prohibited item detection is still not fully satisfied, especially in pictures obtained under special imaging conditions. For noisy X-ray images with heavy occlusion, a direct and suitable approach of representation learning is the optimal solution. According to our study, we realized that heterogeneous information fusion from different extraction approaches can be applied effectively to overcome this issue. We proposed two innovative algorithms to extract effective features of X-ray objects to significantly improve the efficiency of X-ray prohibited item detection. The brief model we proposed fuses the representations learned from the noisy X-ray images and outperforms the best model (DOAM-O) so far on OPIXray. Furthermore, the attention module we designed to select information on deep learning and representation strengthens the model; considering this, the model utilizes lesser time for both training and inference, which makes it easier to be trained on a lightweight computing device.
Funder
National Natural Science Foundation of China
Publisher
Frontiers Media SA
Subject
Physical and Theoretical Chemistry,General Physics and Astronomy,Mathematical Physics,Materials Science (miscellaneous),Biophysics
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