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Measuring the Structure, Composition, and Change of Underwater Environments with Large-area Imaging

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A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection.

Kaibing Xie¹, Jian Yang², Kang Qiu³

¹Peng Cheng Laboratory, Shenzhen, China. xiekb@pcl.ac.cn.

Scientific Data

|December 1, 2022

Summary

Researchers introduce the Underwater Acoustic Target Detection (UATD) dataset, a novel resource for multibeam forward-looking sonar (MFLS) object detection. This dataset aids artificial intelligence research in underwater environments.

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Area of Science:

Robotics and Automation
Marine Technology
Computer Vision

Background:

Multibeam forward-looking sonar (MFLS) is crucial for underwater detection.
Existing research faces challenges due to a lack of available datasets and unsuitable image formats for AI.
Sonar images are often processed for human visual habits, hindering AI applications.

Purpose of the Study:

To address the limitations in underwater object detection research using MFLS.
To introduce a comprehensive and novel dataset for training and evaluating AI models.
To facilitate advancements in automated underwater target recognition.

Main Methods:

Development of the Underwater Acoustic Target Detection (UATD) dataset.
Collection of over 9000 MFLS images using Tritech Gemini 1200ik sonar.
Annotation of 10 target object categories (e.g., cube, cylinder, tyres) in raw sonar image data.
Data collection in lake and shallow water environments.

Main Results:

The UATD dataset comprises a substantial collection of annotated MFLS images.
The dataset includes raw sonar data, beneficial for diverse AI model development.
Benchmarks were established by applying state-of-the-art detectors to the UATD dataset, assessing accuracy and efficiency.

Conclusions:

The UATD dataset provides a valuable resource for advancing underwater object detection research.
The dataset enables the development and validation of AI algorithms for MFLS data.
The established benchmarks offer a baseline for future performance improvements in automated sonar target recognition.