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

Bhowmik, N.; Breckon, T.P.

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, December). Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery. Presented at International Conference on Machine Learning Applications, Bahamas

• Presentation Conference Type: Conference Paper (published)
• Conference Name: International Conference on Machine Learning Applications
• 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
• Public URL: https://durham-repository.worktribe.com/output/1135216
• Publisher URL: https://ieeexplore.ieee.org/xpl/conhome/1001544/all-proceedings
• Related Public URLs: https://doi.org/10.48550/arXiv.2210.16453