Validating the quality of millimeter-wave images input to deep-learning-based threat detection systems

Praful Gupta; Marius B. Facktor; Jack L. Glover; Alan C. Bovik

doi:10.1117/12.2585639

12 April 2021 Validating the quality of millimeter-wave images input to deep-learning-based threat detection systems

Praful Gupta, Marius B. Facktor, Jack L. Glover, Alan C. Bovik

Proceedings Volume 11729, Automatic Target Recognition XXXI; 117290Q (2021) https://doi.org/10.1117/12.2585639
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Deep-learning-based automatic image decision systems are increasingly being relied on to analyze imagery that was previously only viewed and interpreted by humans. Here, we present a real-time method of validating the quality of images input to image decision systems. We focus on the detection of concealed contraband in millimeter-wave (MMW) images of screened people, but the method is general enough to be useful for other applications, such as medical image analysis systems. In applications of such critical importance, it is imperative that automatic target recognition (ATR) algorithms behave predictably and robustly. For example, a MMW system deployed in an airport could suffer from changes in image quality due to a variety of factors, for example, partial hardware malfunctions, excessive vibration, or lack of maintenance or calibration. In such scenarios, it would be desirable to be able to detect changes in image quality immediately when they happen. We investigate the performance of a deep-learning-based ATR when fed with variable quality input images. We describe a first-of-its-kind method to validate the quality of images input to an ATR. The real-time method uses statistical measurements intrinsic to natural images to assess the similarity between an input image and a set of training images. We show, through multiple experiments, that ATR performance is poorer for images with quality that is different than the training set, whether the quality is better or worse. The method is successfully demonstrated as a training-free validation tool for ATR algorithms using two state-of-the-art deep-learning architectures.

Conference Presentation

Citation Download Citation

Praful Gupta, Marius B. Facktor, Jack L. Glover, and Alan C. Bovik "Validating the quality of millimeter-wave images input to deep-learning-based threat detection systems", Proc. SPIE 11729, Automatic Target Recognition XXXI, 117290Q (12 April 2021); https://doi.org/10.1117/12.2585639

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