Adaptability of Computer Vision at the Tactical Edge: Addressing Environmental Uncertainty

Computer Vision (CV) systems are increasingly being adopted into Command and Control (C2) systems to improve intelligence analysis on the battlefield, the tactical edge. CV systems leverage Artificial Intelligence (AI) algorithms to help visualize and interpret the environment, enhancing situational awareness. However, the adaptability of CV systems at the tactical edge remains challenging due to rapidly changing environments and objects which can confuse the deployed models. A CV model leveraged in this environment can become uncertain in its predictions, as the environment and the objects existing in the environment begin to change. Additionally, mission objectives can rapidly change leading to adjustments in technology, camera angles, and image resolutions. All of which can negatively affect the performance of and potentially introduce uncertainty into the system. When the training environment and/or technology differs from the deployment environment, CV models can perform unexpectedly. Unfortunately, most scenarios at the tactical edge do not incorporate Uncertainty Quantification (UQ) into their deployed C2 and CV systems. This concept paper explores the idea of synchronizing robust data operations and model fine-tuning driven by UQ all at the tactical edge. Specifically, curating datasets and training child models based on the residuals of predictions, using these child models to calculate prediction intervals (PI), and then using these PI to calibrate the deployed models. By incorporating UQ into the core operations surrounding C2 and CV systems at the tactical edge, we can help drive purposeful adaptability on the battlefield.