Action Learning for Coral Detection and Species Classification

This paper presents a method for exploring and monitoring coral reef habitats using an autonomous underwater vehicle (AUV) equipped with an onboard camera. To accomplish this task, the vehicle needs to learn to detect and classify different coral species, and also make motion decisions for exploring larger unknown areas while trying to detect as more corals (with species labels) as possible. We propose a systematic framework that integrates object detection, occupancy grid mapping, and reinforcement learning methods. To enable the vehicle to adjudicate decisions between exploration of larger space and exploitation of promising areas, we propose a reward function that combines both an information-theoretic objective for environment spatial coverage and an ingredient that encourages coral detection. We have validated the proposed method through extensive simulations, and the results show that our approach can achieve a good performance even by training with a small number of images (50 images in total) collected in the simulator.