Multi-Modal Detection of Chemical Threats Using Deep Learning - Phase II

This SBIR Phase II project proposes development of deep learning based chemical threat detection and localization framework that exploits multiple sensor data streams including imaging and chemical sensors. The proposed technologies combine deep hypernetworks and reinforcement learning techniques to fuse sensor data streams in an online fashion. The Phase II effort will focus on developing an end-to-end system for real-time applications and demonstrating the capabilities in representative scenarios. Novateur team will utilize data collected in realistic threat scenarios and by performing simulation in a variety of configurations including different type of chemical threats with environmental factors into consideration. The proposed effort will build upon the technologies developed during Phase I of this project and will leverage expertise of the Novateur Team in the areas of deep learning detection and classification technologies, robotic navigation and localization in unknown environments, sensor fusion, statistical relational learning and inference, optimization and development of deep learning algorithms for SWaP constrained mobile platforms, and CBRNE.