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

Source: [A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection](<https://www.sota2.com/research/paper/68fa6e251e209131cea3e234>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T00:51:11.369Z

Authors: Kaibing Xie, Jian Yang, Kang Qiu (1) \_\_INSTITUTION\_3\_\_ Peng Cheng Laboratory, Shenzhen, China)

Published: 2022-12-01T08:26:03.000Z

## 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.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2212.00352>)

[PDF](<https://arxiv.org/pdf/2212.00352.pdf>)