Bridging the gap from academia to application: rapid algorithm design and deployment for artificial intelligence

Machine Learning (ML) and Artificial intelligence (AI) have led to an increase in automation potential within defense applications such as border protection, compound security, and surveillance applications. Recent academic advances in deep learning aided computer vision have yielded impressive results on object detection, and recognition, necessary capabilities to increase automation in defense applications. These advances are often open-sourced, enabling the opportunistic integration of state-of-the-art (SOTA) algorithms into real systems. However, these academic achievements do not translate easily to engineered systems. Academics often are looking at a single capability with metrics such as accuracy or F1 score without consideration of system-level performance and how these algorithms must integrate or what level of computational performance is required. An engineered system is developed as a system of algorithms that must work in conjunction with each other with deployment constraints. This paper describes a system, called Rapid Algorithm Design and Deployment for Artificial Intelligence (RADD-AITM), developed to enable the rapid development of systems of algorithms incorporating these advances in a modular fashion using networked Application Programming Interfaces (APIs). The inherent modularity mitigates the assumption of monolithic integration within a single ecosystem that creates vendor lock. This monolith assumption does not account for the reality that frameworks are usually targeted toward different types of problems and learning vs inference capabilities. RADD-AI makes no such assumption. If a different framework solves subsets of the system more eloquently, they can be integrated into the larger pipeline. RADD-AI enables the integration of state-of-the-art ML into deployed systems while also supporting the necessary ML engineering tasks, such as transfer learning, to operationalize academic achievements. To motivate how RADD-AI enables applications of ML/AI, we detail how this system is used to implement a defense application, a border surveillance capability, via the integration of detection, recognition, and tracking algorithms. This system, implemented and developed within RADD-AI, utilizes several SOTA models and traditional algorithms within multiple frameworks bridging the gap from academic achievement to fielded system.