Currently, DoD spend many tens of millions of dollars per year developing and testing thermal sensor systems designed for 24/7 day/night surveillance capabilities for a wide variety of tactical scenarios, e.g., detection of buried landmines and IEDs, identification of camouflaged/hidden targets, and night-time facial recognition. The advances in AI&ML are driven by new algorithms, notably deep neural networks (DNN), and the maturation of graphical processing unit (GPU) technology optimized for intensive matrix computations. The latest AI&ML algorithms can be trained relatively quickly on low cost GPUs to perform inference on GPUs in real-time. In particular, deep convolutional neural networks (CNN) have demonstrated their potential for accurate object detection and classification. In order to exploit these advances in polarimetric imaging and AI&ML, the team proposes development of an integrated multimodal thermal imaging and data exploitation system designed to provide real-time scene understanding and situational awareness. Such a system would greatly reduce the time and cost required to bring soldier specific image based solutions to the battlefield.
