Deep learning-based visual detection of marine organisms: A survey

Abstract

Most recently, deep learning-based visual detection has attracted rapidly increasing attention paid to marine organisms, thereby expecting to significantly benefit ocean ecology. Suffering from underwater visual degradation including low contrast, color distortion and blur, etc., both advances and challenges on visual detection of marine organisms (VDMO) co-exist in the literature. In this survey, deep learning-based VDMO techniques are comprehensively revisited from a systematic viewpoint covering advances in underwater image preprocessing, deep learning-based detection approaches, benchmark dataset and intensively quantitative comparisons. Furthermore, in terms of inherent features of marine organisms and complexity of underwater visual environments, underlying challenges are unfolded in depth. Such a self-contained survey is expected to exploit potential breakthroughs and explore probable trends in deep learning-based VDMO techniques.