Multimodal learning based threat detection in dual view, dual energy  x-ray images

Renu M. Rameshan; Somya Goel; Kuldeep Singh; Krishan Sharma; Anoop G. Prabhu

doi:10.1117/12.3013085

7 June 2024 Multimodal learning based threat detection in dual view, dual energy x-ray images

Renu M. Rameshan, Somya Goel, Kuldeep Singh, Krishan Sharma, Anoop G. Prabhu

Proceedings Volume 13033, Multimodal Image Exploitation and Learning 2024; 1303306 (2024) https://doi.org/10.1117/12.3013085
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Abstract

Explosive detection in dual energy x-ray systems is a difficult problem owing to the fact that we don’t have enough information to estimate the effective atomic number and density of a material. Though there are several approximations available in the literature, building a solution with an acceptable true positive and false positive rate is not trivial. In this work we exploit the learning capability of a multimodal neural network for achieving a high detection rate and an acceptable false positive rate. We also show that, using a guided filter based fusion for fusing the high and low energy images leads to fused images that have a high mutual information w.r.t. the high and low images, than the existing solutions. This fused image is one of the inputs to the neural network, the other being a material dependent image that we create from the high and low energy images. The proposed solution has a high recall.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Renu M. Rameshan, Somya Goel, Kuldeep Singh, Krishan Sharma, and Anoop G. Prabhu "Multimodal learning based threat detection in dual view, dual energy x-ray images", Proc. SPIE 13033, Multimodal Image Exploitation and Learning 2024, 1303306 (7 June 2024); https://doi.org/10.1117/12.3013085

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