Detect and follow a custom object, using OBSEA underwater crawler

Abstract

The strategic advancement of monitoring protocols relies on cabled marine observatories for obtaining real-time multiparametric biological and environmental data. The integration of docked mobile platforms, such as underwater crawlers, proves beneficial, extending the surveillance radius of underwater observatories and enhancing their overall performance and functionality. Normally, underwater crawlers are controlled manually, resulting in considerable time and cost expenditures for both crawler operation and monitoring seabed species. To overcome this challenge, the OBSEA Underwater crawler was employed to detect and track a custom object in the laboratory environment. The detection process involved the preparation of a dataset, training a model with YOLO, and finally utilizing an algorithm to track the custom object.