Integrated 2-Color Thermal Polarimetric Sensor and Deep Neural Network for AI and ML Based Detection and Identification

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.