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Updated: Aug 1, 2025
09:19
Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging
Published on: April 18, 2025
678
Modified you-only-look-once model for joint source detection and azimuth estimation in a multi-interfering underwater
Meng Zhao1, Wenbo Wang1, Qunyan Ren1
1Key Laboratory of Underwater Acoustic Environment, Chinese Academy of Sciences, Beijing 100190, China.
The Journal of the Acoustical Society of America
|April 24, 2023
Summary
A modified You-Only-Look-Once (M-YOLO) model effectively detects underwater acoustic sources and estimates their azimuth. This novel approach demonstrates high accuracy in complex, noisy environments, achieving near-perfect detection rates.
Area of Science:
- Underwater acoustics
- Signal processing
- Machine learning
Background:
- Object detection in complex images is analogous to underwater source detection in acoustic data.
- Traditional methods struggle with multi-interfering acoustic environments.
Purpose of the Study:
- To adapt the You-Only-Look-Once (YOLO) model for joint source detection and azimuth estimation in underwater acoustics.
- To develop a robust method for identifying and localizing acoustic targets amidst interference.
Main Methods:
- The You-Only-Look-Once (YOLO) model was modified into a Modified YOLO (M-YOLO) model.
- Input data was transformed into a frequency-beam domain (FBD) sample.
- M-YOLO utilized a single-regression neural network to process FBD samples.
Main Results:
- M-YOLO demonstrated strong robustness against varying signal-to-noise ratios and ocean conditions in simulations.
- Real-world multi-interfering environment tests showed near-100% target detection.
- Azimuth estimation achieved a root mean square error of 0.54°.
Conclusions:
- The M-YOLO model is a highly effective tool for joint source detection and azimuth estimation in challenging underwater acoustic scenarios.
- This deep learning approach offers significant improvements in accuracy and robustness over conventional methods.
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