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

arXiv: https://arxiv.org/abs/2212.00352 · HTML: https://mlsanity.com/paper/2212.00352/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 9 of 9)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [YOLOv3: An Incremental Improvement](https://mlsanity.com/paper/1804.02767/) (2018)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Semantic Segmentation of Underwater Imagery: Dataset and Benchmark](https://mlsanity.com/paper/2004.01241/) (2020)
- [A Dataset And Benchmark Of Underwater Object Detection For Robot Picking](https://mlsanity.com/paper/2106.05681/) (2021)
- [The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation](https://mlsanity.com/paper/2108.06800/) (2021)
- [Self-supervised Learning for Sonar Image Classification](https://mlsanity.com/paper/2204.09323/) (2022)
- [Cross-view and Cross-domain Underwater Localization based on Optical Aerial and Acoustic Underwater Images](https://mlsanity.com/paper/2202.07817/) (2022)
- [A rasterized ray-tracer pipeline for real-time, multi-device sonar simulation](https://mlsanity.com/paper/2001.03539/) (2020)

## Built on by (most cited first, 11 of 11)

- [RGB-Sonar Tracking Benchmark and Spatial Cross-Attention Transformer Tracker](https://mlsanity.com/paper/2406.07189/) (2024)
- [SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Survey](https://mlsanity.com/paper/2411.00172/) (2024)
- [Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challenges](https://mlsanity.com/paper/2412.11840/) (2024)
- [Sonar Image Datasets: A Comprehensive Survey of Resources, Challenges, and Applications](https://mlsanity.com/paper/2510.03353/) (2025)
- [uScenes: A Multimodal RGB and 3D Sonar Dataset for Underwater Robot Perception](https://mlsanity.com/paper/2608.27795/) (2026)
- [Underwater object detection in sonar imagery with detection transformer and Zero-shot neural architecture search](https://mlsanity.com/paper/2505.06694/) (2025)
- [Towards Training-Free Underwater 3D Object Detection from Sonar Point Clouds: A Comparison of Traditional and Deep Learning Approaches](https://mlsanity.com/paper/2508.18293/) (2025)
- [MambaDSF: Multi-Scale SSM with Dilated Feature Fusion for Sonar Small Target Detection](https://mlsanity.com/paper/2605.24928/) (2026)
- [Spiking Neural Networks for Energy-Efficient Object Detection in Forward-Looking Sonar Imagery](https://mlsanity.com/paper/2608.22072/) (2026)
- [Quantum-Gated LiteSSD: A Parameter-Efficient Lightweight Hybrid Quantum-Classical Framework for Forward-Looking Sonar Object Detection](https://mlsanity.com/paper/2609.14025/) (2026)
- [Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perception](https://mlsanity.com/paper/2609.18100/) (2026)

## Abstract (arXiv)

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.