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A deep learning framework based on structured space model for detecting small objects in complex underwater
Yaoming Zhuang1, Jiaming Liu2, Haoyang Zhao2,3
1Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China. zhuangyaoming@mail.neu.edu.cn.
Communications Engineering
|February 17, 2025
Summary
We developed UWNet, a lightweight underwater detection model, to accurately identify marine life like starfish and scallops. This model enhances efficiency for underwater robots, improving marine ecosystem monitoring.
Area of Science:
- Marine Biology
- Computer Vision
- Robotics
Background:
- Effective marine ecosystem monitoring relies on accurate underwater target detection.
- Current methods face challenges in balancing detection accuracy, model efficiency, and real-time performance.
- Small marine organisms are particularly difficult to detect in underwater environments.
Purpose of the Study:
- To propose an innovative approach for small target detection in underwater environments.
- To develop a high-accuracy, lightweight detection model for marine life monitoring.
- To improve the efficiency and applicability of underwater detection models for robotic deployment.
Main Methods:
- Combined the Structured Space Model (SSM) with feature enhancement techniques.
- Developed a lightweight detection model named UWNet.
- Evaluated UWNet's performance on detecting small marine organisms like starfish and scallops.
Main Results:
- UWNet demonstrated excellent detection accuracy, especially for challenging organisms.
- The model significantly reduced parameters (5% to 390%) compared to other models.
- Achieved substantial improvements in computational efficiency while maintaining high detection accuracy.
Conclusions:
- UWNet offers a superior solution for underwater small target detection.
- The model's lightweight design makes it suitable for deployment on underwater robots.
- This advancement aids in more effective marine life monitoring and ecosystem preservation.
