Marine Event Vision: Harnessing Event Cameras for Robust Object Detection in Marine Scenarios

Automated object detection in marine environments presents unique challenges due to factors such as water turbidity, variable lighting, natural camouflage, dynamic object movements, sensor limitations, noise, and environmental influences like currents and biofouling. This paper addresses these issues by introducing a novel approach for detecting moving objects in marine settings, leveraging event-based cameras and an event-driven Generalized Hough Transform (GHT) within a template-matching framework enhanced by a clustering algorithm. The approach is validated through qualitative results demonstrating object detection in the water column and on the surface, as well as quantitative analysis of moving objects. The results high-light the robustness and versatility of the method, extending its applicability beyond marine environments.