On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery

Bhowmik, N.; Gaus, Y.F.A.; Akcay, S.; Barker, J.W.; Breckon, T.P.

Abstract

X-ray security screening is in widespread use to maintain transportation security against a wide range of potential threat profiles. Of particular interest is the recent focus on the use of automated screening approaches, including the potential anomaly detection as a methodology for concealment detection within complex electronic items. Here we address this problem considering varying segmentation strategies to enable the use of both object level and sub-component level anomaly detection via the use of secondary convolutional neural network (CNN) architectures. Relative performance is evaluated over an extensive dataset of exemplar cluttered X-ray imagery, with a focus on consumer electronics items. We find that sub-component level segmentation produces marginally superior performance in the secondary anomaly detection via classification stage, with true positive of ~98% of anomalies, with a ~3% false positive.

Citation

Bhowmik, N., Gaus, Y., Akcay, S., Barker, J., & Breckon, T. (2019, December). On the Impact of Object and Sub-Component Level Segmentation Strategies for Supervised Anomaly Detection within X-Ray Security Imagery. Presented at 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019), Boca Raton, Florida, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 18th IEEE International Conference on Machine Learning and Applications (ICMLA 2019)
• Start Date: Dec 16, 2019
• End Date: Dec 19, 2019
• Acceptance Date: Sep 21, 2019
• Online Publication Date: Feb 17, 2020
• Publication Date: 2019
• Deposit Date: Dec 20, 2019
• Publicly Available Date: Jun 4, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 986-991
• Book Title: 2019 18th IEEE International Conference On Machine Learning And Applications (ICMLA), Boca Raton, FL, USA, 2019.
• DOI: https://doi.org/10.1109/icmla.2019.00168
• Public URL: https://durham-repository.worktribe.com/output/1142767