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

TL;DR

This paper introduces the UATD dataset, comprising over 9000 multibeam forward-looking sonar images with annotations, to advance underwater object detection research and facilitate AI development in this domain.

Contribution

The paper presents a novel, publicly available underwater sonar dataset with detailed annotations, addressing the lack of data and transforming sonar images for AI research.

Findings

Benchmark results for state-of-the-art detectors on UATD

Demonstration of dataset's practicality in underwater detection

Improved accuracy and efficiency in object detection tasks

Abstract

Multibeam forward-looking sonar (MFLS) plays an important role in underwater detection. There are several challenges to the research on underwater object detection with MFLS. Firstly, the research is lack of available dataset. Secondly, the sonar image, generally processed at pixel level and transformed to sector representation for the visual habits of human beings, is disadvantageous to the research in artificial intelligence (AI) areas. Towards these challenges, we present a novel dataset, the underwater acoustic target detection (UATD) dataset, consisting of over 9000 MFLS images captured using Tritech Gemini 1200ik sonar. Our dataset provides raw data of sonar images with annotation of 10 categories of target objects (cube, cylinder, tyres, etc). The data was collected from lake and shallow water. To verify the practicality of UATD, we apply the dataset to the state-of-the-art detectors and provide corresponding benchmarks for its accuracy and efficiency.