A Comprehensive Review on Underwater Object Detection Techniques

Abstract

Recent years have seen a surge in interest in underwater object detection, driven by increased participation in marine research, environmental monitoring, and underwater robotics. This paper offers a systematic review of recent advancements in this field, covering both image enhancement and object detection techniques. The review starts with an examination of state-of-the-art underwater image enhancement methods, including selective color attenuation and feature enhancement modules. Object detection approaches are then explored, ranging from lightweight neural networks for contaminant detection to complex multi-layer models for tiny object features. The paper provides valuable insights for researchers and practitioners, summarizing key themes and results while outlining the strengths and limitations of each approach.