Review On Deep Learning Technique For Underwater Object Detection

TL;DR

This paper reviews deep learning techniques for underwater object detection, discussing datasets, algorithms, and their advantages and disadvantages in challenging underwater environments.

Contribution

It provides a comprehensive overview of datasets and algorithms used in underwater object detection, highlighting their strengths and limitations.

Findings

Deep learning improves underwater detection accuracy

Challenges include light scattering and absorption effects

Various datasets and algorithms have been evaluated

Abstract

Repair and maintenance of underwater structures as well as marine science rely heavily on the results of underwater object detection, which is a crucial part of the image processing workflow. Although many computer vision-based approaches have been presented, no one has yet developed a system that reliably and accurately detects and categorizes objects and animals found in the deep sea. This is largely due to obstacles that scatter and absorb light in an underwater setting. With the introduction of deep learning, scientists have been able to address a wide range of issues, including safeguarding the marine ecosystem, saving lives in an emergency, preventing underwater disasters, and detecting, spooring, and identifying underwater targets. However, the benefits and drawbacks of these deep learning systems remain unknown. Therefore, the purpose of this article is to provide an overview of the dataset that has been utilized in underwater object detection and to present a discussion of the advantages and disadvantages of the algorithms employed for this purpose.