Artificial Intelligence on the Edge for Enabling Reliable Affordable Mass

Unmanned Aerial Vehicles (UAVs) are increasingly deployed within reconnaissance areas to collect sensor data. This tremendous amount of data is often uploaded to a central base station for analyzing and identifying potential targets. However, centralized approaches to Automatic Target Recognition (ATR) introduce problems such as network communication delays and susceptibility of the system to malicious attacks, thereby limiting autonomous capabilities. To overcome these challenges, we have developed an approach for artificial intelligence on the edge to enable reliable affordable mass. Specifically, we have developed a Continuous Deployment for Machine Learning (CD4ML) framework for operationalizing ML approaches, incorporating capabilities for inferencing, drift detection, model adaptation, and performance verification. We have deployed this framework on a real UAV to enable vision-based feedback control for ATR applications. The autonomy of the UAV is demonstrated in our laboratory for detecting, identifying, and tracking a ground vehicle. Finally, we have also developed a semi-automated, reusable, and modular workflow for the deployment of AI at the edge on target FPGA environments, as well as certification tools that will aid in further maturation of our developed approach for ATR applications.