Experimental study of underwater operation scene with target perception framework

Abstract

This paper presents a target perception framework aimed at enhancing diver safety and facilitating underwater operations by extracting critical information from underwater scenes. The framework employs a layered processing approach, which encompasses water column imaging, constant false alarm rate detection, and local feature analysis. To simulate the diver's underwater environment, we conducted experiments with three distinct fields of view: fixed down-looking, fixed front-looking, and mobile side-looking perspectives. Our experimental findings demonstrate the framework's ability to accurately differentiate between false targets, stationary targets, and moving targets within the underwater scenes, as well as to capture the motion trajectories of dynamic targets. Furthermore, the application of 3D reconstruction techniques to underwater scene data enables the generation of approximate stereoscopic representations of divers and bubble groups.