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Deep artificial neural networks, trained with labeled data sets are widelyused in numerous vision and robotics applications today. In terms of AI, theseare called reflex models, referring to the fact that they do not self-evolve oractively adapt to environmental changes. As demand for intelligent robotcontrol expands to many high level tasks, reinforcement learning and statebased models play an increasingly important role. Herein, in computer visionand robotics domain, we study a novel approach to add reinforcement controlsonto the image recognition reflex models to attain better overall performance,specifically to a wider environment range beyond what is expected of the taskreflex models. Follow a common infrastructure with environment sensing and AIbased modeling of self-adaptive agents, we implement multiple types of AIcontrol agents. To the end, we provide comparative results of these agents withbaseline, and an insightful analysis of their benefit to improve overall imagerecognition performance in real world.

AI Online Filters to Real World Image Recognition