AI Online Filters to Real World Image Recognition
This work addresses the need for more adaptive AI in computer vision and robotics, though it appears incremental by combining existing methods.
The paper tackles the problem of reflex models in image recognition not adapting to environmental changes by adding reinforcement learning controls to improve performance across a wider range of environments, providing comparative results and analysis of these agents against a baseline.
Deep artificial neural networks, trained with labeled data sets are widely used in numerous vision and robotics applications today. In terms of AI, these are called reflex models, referring to the fact that they do not self-evolve or actively adapt to environmental changes. As demand for intelligent robot control expands to many high level tasks, reinforcement learning and state based models play an increasingly important role. Herein, in computer vision and robotics domain, we study a novel approach to add reinforcement controls onto the image recognition reflex models to attain better overall performance, specifically to a wider environment range beyond what is expected of the task reflex models. Follow a common infrastructure with environment sensing and AI based modeling of self-adaptive agents, we implement multiple types of AI control agents. To the end, we provide comparative results of these agents with baseline, and an insightful analysis of their benefit to improve overall image recognition performance in real world.