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Automatic and Robust Object Detection in X-Ray Baggage Inspection Using Deep Convolutional Neural Networks | IEEE Journals & Magazine | IEEE Xplore

Automatic and Robust Object Detection in X-Ray Baggage Inspection Using Deep Convolutional Neural Networks


Abstract:

For the purpose of ensuring public security, automatic inspection of X-ray scanners has been deployed at the entry points of many public places to detect dangerous object...Show More

Abstract:

For the purpose of ensuring public security, automatic inspection of X-ray scanners has been deployed at the entry points of many public places to detect dangerous objects. However, current surveillance systems cannot function without human supervision and intervention. In this article, we propose an effective method using deep convolutional neural networks to detect objects during X-ray baggage inspection. As a first step, a large amount of training data is generated by a specific data augmentation technique. Second, a feature enhancement module is used to improve feature extraction capabilities. Then, in order to address the foreground–background imbalance in the region proposal network, focal loss is adopted. Third, the multiscale fused region of interest is utilized to obtain more robust proposals. Finally, soft nonmaximum suppression is adopted to alleviate overlaps in baggage detection. As compared with existing algorithms, the proposed method proves that it is more accurate and robust when dealing with densely cluttered backgrounds during X-ray baggage inspection.
Published in: IEEE Transactions on Industrial Electronics ( Volume: 68, Issue: 10, October 2021)
Page(s): 10248 - 10257
Date of Publication: 29 September 2020

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