# UTD-Yolov5: A Real-time Underwater Targets Detection Method based on Attention Improved YOLOv5

arXiv: https://arxiv.org/abs/2207.00837 · HTML: https://mlsanity.com/paper/2207.00837/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 37 references that are in the map.
- First posted on arXiv 2 Jul 2022.

## Builds on (most cited first, 13 of 13)

- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [YOLOv4: Optimal Speed and Accuracy of Object Detection](https://mlsanity.com/paper/2004.10934/) (2020)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [CornerNet: Detecting Objects as Paired Keypoints](https://mlsanity.com/paper/1808.01244/) (2018)
- [Bottom-up Object Detection by Grouping Extreme and Center Points](https://mlsanity.com/paper/1901.08043/) (2019)
- [Attention Branch Network: Learning of Attention Mechanism for Visual Explanation](https://mlsanity.com/paper/1812.10025/) (2018)
- [SNIPER: Efficient Multi-Scale Training](https://mlsanity.com/paper/1805.09300/) (2018)
- [Face Detection with the Faster R-CNN](https://mlsanity.com/paper/1606.03473/) (2016)
- [RRPN: Radar Region Proposal Network for Object Detection in Autonomous Vehicles](https://mlsanity.com/paper/1905.00526/) (2019)
- [An Implementation of Faster RCNN with Study for Region Sampling](https://mlsanity.com/paper/1702.02138/) (2017)
- [TridentNet: A Conditional Generative Model for Dynamic Trajectory Generation](https://mlsanity.com/paper/2101.06374/) (2021)
- [The CSIRO Crown-of-Thorn Starfish Detection Dataset](https://mlsanity.com/paper/2111.14311/) (2021)

## Built on by (most cited first, 2 of 2)

- [YOLOv5 vs. YOLOv8 in Marine Fisheries: Balancing Class Detection and Instance Count](https://mlsanity.com/paper/2405.02312/) (2024)
- [Less yet robust: crucial region selection for scene recognition](https://mlsanity.com/paper/2409.14741/) (2024)

## Abstract (arXiv)

As the treasure house of nature, the ocean contains abundant resources. But the coral reefs, which are crucial to the sustainable development of marine life, are facing a huge crisis because of the existence of COTS and other organisms. The protection of society through manual labor is limited and inefficient. The unpredictable nature of the marine environment also makes manual operations risky. The use of robots for underwater operations has become a trend. However, the underwater image acquisition has defects such as weak light, low resolution, and many interferences, while the existing target detection algorithms are not effective. Based on this, we propose an underwater target detection algorithm based on Attention Improved YOLOv5, called UTD-Yolov5. It can quickly and efficiently detect COTS, which in turn provides a prerequisite for complex underwater operations. We adjusted the original network architecture of YOLOv5 in multiple stages, including: replacing the original Backbone with a two-stage cascaded CSP (CSP2); introducing the visual channel attention mechanism module SE; designing random anchor box similarity calculation method etc. These operations enable UTD-Yolov5 to detect more flexibly and capture features more accurately. In order to make the network more efficient, we also propose optimization methods such as WBF and iterative refinement mechanism. This paper conducts a lot of experiments based on the CSIRO dataset [1]. The results show that the average accuracy of our UTD-Yolov5 reaches 78.54%, which is a great improvement compared to the baseline.