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Fast Underwater Optical Beacon Finding and High Accuracy Visual Ranging Method Based on Deep Learning.

Bo Zhang¹, Ping Zhong¹, Fu Yang¹

¹College of Science, Donghua University, Shanghai 201620, China.

Sensors (Basel, Switzerland)

|October 27, 2022

Summary

This summary is machine-generated.

This study introduces a faster, more accurate method for autonomous underwater vehicle (AUV) docking using enhanced visual recognition of small optical beacons. The new approach improves detection range and precision for reliable underwater navigation.

Keywords:

autonomous underwater vehicles deep learning monocular vision target detection

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Area of Science:

Robotics and Computer Vision
Underwater Navigation and Sensing

Background:

Autonomous underwater vehicle (AUV) docking relies on visual recognition and localization of optical beacons.
Challenges include light attenuation, surface reflections, and small beacon size, limiting monocular vision range.

Purpose of the Study:

To develop a fast monocular camera localization method for small, 4-light underwater optical beacons.
To enhance the accuracy and detection range for improved AUV navigation.

Main Methods:

A YOLO V5 (You Only Look Once) model integrated with coordinated attention (CA) mechanisms was developed.
A sub-pixel light source centroid localization method was proposed, combining super-resolution generative adversarial networks (SRGAN) for image enhancement and Zernike moments.

Main Results:

The proposed YOLO V5-CA model achieved 96.1% prediction accuracy and 95.1% recall.
The detection range for small optical beacons was extended from 7m to 10m.
The method demonstrated an average relative distance error of 1.04% and a detection speed of 0.088s (11.36 FPS).

Conclusions:

The study presents a robust solution for long-distance, fast, and accurate positioning of underwater optical beacons.
The developed method offers significant improvements in recognition speed, ranging accuracy, and detection range for AUV applications.