Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items

Bhowmik, N.; Gaus, Y.F.A.; Breckon, T.P.

Abstract

X-ray baggage security screening is widely used to maintain aviation and transport safety and security. To address the future challenges of increasing volumes and complexities, the recent focus on the use of automated screening approaches are of particular interest. This includes the potential for automatic threat detection as a methodology for concealment detection within complex electronics and electrical items screened using low-cost, 2D X-ray imagery (single or multiple view). In this work, we use automatic object segmentation algorithms enabled by deep Convolutional Neural Networks (CNN, e.g. Mask R-CNN) together with the concept of image over-segmentation to the sub-component level and subsequently use CNN classification to determine the presence of anomalies at both an object or sub-component level. We evaluate the performance impact of three strategies: full frame, object segmentation, and object over-segmentation, for threat/anomaly detection within consumer electronics items. The experimental results exhibit that the object over-segmentation produces superior performance for the anomaly detection via classification, with $< 5\%$ false positive and ~99% true positive.

Citation

Bhowmik, N., Gaus, Y., & Breckon, T. (2019, November). Using Deep Neural Networks to Address the Evolving Challenges of Concealed Threat Detection within Complex Electronic Items. Presented at 2019 IEEE International Symposium on Technologies for Homeland Security, Boston, USA

• Presentation Conference Type: Conference Paper (published)
• Conference Name: 2019 IEEE International Symposium on Technologies for Homeland Security
• Start Date: Nov 5, 2019
• End Date: Nov 6, 2019
• Acceptance Date: Aug 30, 2019
• Online Publication Date: Mar 12, 2020
• Publication Date: 2019
• Deposit Date: Dec 20, 2019
• Publicly Available Date: Mar 19, 2020
• Publisher: Institute of Electrical and Electronics Engineers
• Pages: 1-6
• Book Title: Proceeding of the International Symposium on Technologies for Homeland Security.
• DOI: https://doi.org/10.1109/hst47167.2019.9032920
• Public URL: https://durham-repository.worktribe.com/output/1141306