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Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery

Bhowmik, N.; Breckon, T.P.

Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery Thumbnail


Authors



Abstract

X-ray baggage security screening is in widespread use and crucial to maintaining transport security for threat/anomaly detection tasks. The automatic detection of anomaly, which is concealed within cluttered and complex electronics/electrical items, using 2D X-ray imagery is of primary interest in recent years. We address this task by introducing joint object sub-component level segmentation and classification strategy using deep Convolution Neural Network architecture. The performance is evaluated over a dataset of cluttered X-ray baggage security imagery, consisting of consumer electrical and electronics items using variants of dual-energy X-ray imagery (pseudo-colour, high, low, and effective-Z). The proposed joint sub-component level segmentation and classification approach achieve ∼ 99% true positive and ∼ 5% false positive for anomaly detection task.

Citation

Bhowmik, N., & Breckon, T. (2022). Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery.

Conference Name International Conference on Machine Learning Applications
Conference Location Bahamas
Start Date Dec 12, 2022
End Date Dec 14, 2022
Acceptance Date Sep 5, 2022
Publication Date 2022-12
Deposit Date Nov 2, 2022
Publicly Available Date Dec 15, 2022
Publisher Institute of Electrical and Electronics Engineers
Publisher URL https://ieeexplore.ieee.org/xpl/conhome/1001544/all-proceedings
Related Public URLs https://doi.org/10.48550/arXiv.2210.16453

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Accepted Conference Proceeding (6.2 Mb)
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