Development of an intelligent underwater recognition system based on the deep reinforcement learning algorithm in an autonomous underwater vehicle

This study's objective was to design an intelligent underwater recognition system and apply it in an autonomous underwater vehicle (AUV) for the recognition and tracking of underwater objects. The intelligent underwater recognition system predicted the depth map with the stereo matching algorithm based on semi-global block matching (SGBM) through the images of voyage records. It used the Deep Q-Network (DQN) algorithm based on deep reinforcement learning so that the agent may focus on the localization area of objects on the disparity map. Next, the intelligent underwater recognition system performed depth estimation according to the disparity map to obtain the stereo point clouds of the underwater object. After obtaining the depth information, the intelligent underwater recognition system constructed a deep network based on Faster Region-based Convolutional Neural Network (R-CNN) to detect the underwater object. Eventually, the system was successfully verified by a series of diving-depth tracking experiments.