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

• URL: http://arxiv.org/abs/2210.16453v1
• Date: Sat, 29 Oct 2022 00:44:50 GMT
• Title: Joint Sub-component Level Segmentation and Classification for Anomaly Detection within Dual-Energy X-Ray Security Imagery
• Authors: Neelanjan Bhowmik, Toby P. Breckon
• Abstract summary: The performance is evaluated over a dataset of cluttered X-ray baggage security imagery. The proposed joint sub-component level segmentation and classification approach achieve 99% true positive and 5% false positive for anomaly detection task.
• Score: 14.785070524184649
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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