Detection, classification and identification of objects in underwater harsh environments using machine learning

Detection, classification and identification of underwater objects are both complex and essential tasks for perception and navigation of Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs). AUVs need to detect obstacles to avoid them and need to identify certain objects in tasks such as Automatic Target Recognition (ATR) or in manipulation tasks in the case of ROVs. Leveraging a dataset published by the Robotics for Asset Maintenance and Inspection (RAMI) competition for the 2023 Virtual competition, in this work, we detail our machine learning-based solution for the visual detection, classification and identification of several objects including buoys of different colours, markers with numbers, pipes and ring poles among others. The results show excellent performance in training dataset and very good performance for detection and classification in the unknown test dataset showing the goodness of the methods in harsh environments with low visibility. Identification remains a challenging task especially for not previously seen data and requires further work including synthethic data generation.