# Review On Deep Learning Technique For Underwater Object Detection

arXiv: https://arxiv.org/abs/2209.10151 · HTML: https://mlsanity.com/paper/2209.10151/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 67 references that are in the map.
- First posted on arXiv 21 Sep 2022.

## Builds on (most cited first, 16 of 16)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Striving for Simplicity: The All Convolutional Net](https://mlsanity.com/paper/1412.6806/) (2014)
- [Stacked Hourglass Networks for Human Pose Estimation](https://mlsanity.com/paper/1603.06937/) (2016)
- [SphereFace: Deep Hypersphere Embedding for Face Recognition](https://mlsanity.com/paper/1704.08063/) (2017)
- [An Underwater Image Enhancement Benchmark Dataset and Beyond](https://mlsanity.com/paper/1901.05495/) (2019)
- [FractalNet: Ultra-Deep Neural Networks without Residuals](https://mlsanity.com/paper/1605.07648/) (2016)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions](https://mlsanity.com/paper/1901.05320/) (2019)
- [TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris](https://mlsanity.com/paper/2007.08097/) (2020)
- [Aggregate channel features for multi-view face detection](https://mlsanity.com/paper/1407.4023/) (2014)

## Abstract (arXiv)

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.