A Reinforcement Learning Paradigm of Configuring Visual Enhancement for Object Detection in Underwater Scenes

Abstract

This article investigates the problem of enhancing underwater visual observations for the purpose of accurate underwater object detection. Most existing underwater visual enhancement algorithms tend to follow human vision preference but do not necessarily favor the effectiveness of an object detection algorithm. We observe that it should not be the human vision preference but the object detection algorithm that knows what underwater visual enhancement configuration is most beneficial to the detection tasks. In light of this observation, we propose a reinforcement learning paradigm of configuring visual enhancement for object detection in underwater scenes. Specifically, we use underwater image features as states and object detection score increments as rewards. We set up a collection of extensible actions that consist of multiple visual enhancement algorithms. The optimal policy is learned in the form of an action sequence, which characterizes a stepwise process of visual enhancement. Experimental results validate that the sequence of visual enhancement algorithms configured with respect to the object detection algorithm is in favor of improving the detection results.