# A Dataset And Benchmark Of Underwater Object Detection For Robot Picking

arXiv: https://arxiv.org/abs/2106.05681 · HTML: https://mlsanity.com/paper/2106.05681/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 21 references that are in the map.
- First posted on arXiv 10 Jun 2021.

## Builds on (most cited first, 16 of 16)

- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [Understanding deep learning requires rethinking generalization](https://mlsanity.com/paper/1611.03530/) (2016)
- [FCOS: Fully Convolutional One-Stage Object Detection](https://mlsanity.com/paper/1904.01355/) (2019)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Bridging the Gap Between Anchor-based and Anchor-free Detection via Adaptive Training Sample Selection](https://mlsanity.com/paper/1912.02424/) (2019)
- [Bag of Tricks and A Strong Baseline for Deep Person Re-identification](https://mlsanity.com/paper/1903.07071/) (2019)
- [Cascade R-CNN: High Quality Object Detection and Instance Segmentation](https://mlsanity.com/paper/1906.09756/) (2019)
- [Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection](https://mlsanity.com/paper/2006.04388/) (2020)
- [RepPoints: Point Set Representation for Object Detection](https://mlsanity.com/paper/1904.11490/) (2019)
- [Probabilistic Anchor Assignment with IoU Prediction for Object Detection](https://mlsanity.com/paper/2007.08103/) (2020)
- [Feature Selective Anchor-Free Module for Single-Shot Object Detection](https://mlsanity.com/paper/1903.00621/) (2019)
- [FreeAnchor: Learning to Match Anchors for Visual Object Detection](https://mlsanity.com/paper/1909.02466/) (2019)
- [Grid R-CNN](https://mlsanity.com/paper/1811.12030/) (2018)
- [RoIMix: Proposal-Fusion among Multiple Images for Underwater Object Detection](https://mlsanity.com/paper/1911.03029/) (2019)
- [Underwater object detection using Invert Multi-Class Adaboost with deep learning](https://mlsanity.com/paper/2005.11552/) (2020)

## Built on by (most cited first, 30 of 31)

- [Rethinking Few-Shot Object Detection on a Multi-Domain Benchmark](https://mlsanity.com/paper/2207.11169/) (2022)
- [Overload: Latency Attacks on Object Detection for Edge Devices](https://mlsanity.com/paper/2304.05370/) (2023)
- [A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection](https://mlsanity.com/paper/2212.00352/) (2022)
- [Is Underwater Image Enhancement All Object Detectors Need?](https://mlsanity.com/paper/2311.18814/) (2023)
- [AQUA20: A Benchmark Dataset for Underwater Species Classification under Challenging Conditions](https://mlsanity.com/paper/2506.17455/) (2025)
- [Underwater Object Detection in the Era of Artificial Intelligence: Current, Challenge, and Future](https://mlsanity.com/paper/2410.05577/) (2024)
- [A Gated Cross-domain Collaborative Network for Underwater Object Detection](https://mlsanity.com/paper/2306.14141/) (2023)
- [AMSP-UOD: When Vortex Convolution and Stochastic Perturbation Meet Underwater Object Detection](https://mlsanity.com/paper/2308.11918/) (2023)
- [UnitModule: A Lightweight Joint Image Enhancement Module for Underwater Object Detection](https://mlsanity.com/paper/2309.04708/) (2023)
- [MarineDet: Towards Open-Marine Object Detection](https://mlsanity.com/paper/2310.01931/) (2023)
- [A Meta-Learning Approach to Predicting Performance and Data Requirements](https://mlsanity.com/paper/2303.01598/) (2023)
- [UWBench: A Comprehensive Vision-Language Benchmark for Underwater Understanding](https://mlsanity.com/paper/2510.18262/) (2025)
- [Beneath the Surface: The Role of Underwater Image Enhancement in Object Detection](https://mlsanity.com/paper/2411.14626/) (2024)
- [Are All Marine Species Created Equal? Performance Disparities in Underwater Object Detection](https://mlsanity.com/paper/2508.18729/) (2025)
- [ODverse33: Is the New YOLO Version Always Better? A Multi Domain benchmark from YOLO v5 to v11](https://mlsanity.com/paper/2502.14314/) (2025)
- [The Common Objects Underwater (COU) Dataset for Robust Underwater Object Detection](https://mlsanity.com/paper/2502.20651/) (2025)
- [You Sense Only Once Beneath: Ultra-Light Real-Time Underwater Object Detection](https://mlsanity.com/paper/2504.15694/) (2025)
- [ORCA: Object Recognition and Comprehension for Archiving Marine Species](https://mlsanity.com/paper/2512.21150/) (2025)
- [An Underwater Image Semantic Segmentation Method Focusing on Boundaries and a Real Underwater Scene Semantic Segmentation Dataset](https://mlsanity.com/paper/2108.11727/) (2021)
- [A Real-Time Framework for Domain-Adaptive Underwater Object Detection with Image Enhancement](https://mlsanity.com/paper/2403.19079/) (2024)
- [U-DECN: End-to-End Underwater Object Detection ConvNet with Improved DeNoising Training](https://mlsanity.com/paper/2408.05780/) (2024)
- [Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation](https://mlsanity.com/paper/2506.23505/) (2025)
- [E-ConvNeXt: A Lightweight and Efficient ConvNeXt Variant with Cross-Stage Partial Connections](https://mlsanity.com/paper/2508.20955/) (2025)
- [Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via B'ezier Curve Modelling](https://mlsanity.com/paper/2406.13951/) (2024)
- [LUIEO: A Lightweight Model for Integrating Underwater Image Enhancement and Object Detection](https://mlsanity.com/paper/2412.07009/) (2024)
- [An Empirical Study on the Robustness of YOLO Models for Underwater Object Detection](https://mlsanity.com/paper/2509.17561/) (2025)
- [SPMamba-YOLO: An Underwater Object Detection Network Based on Multi-Scale Feature Enhancement and Global Context Modeling](https://mlsanity.com/paper/2602.22674/) (2026)
- [Adaptive Enhancement and Dual-Pooling Sequential Attention for Lightweight Underwater Object Detection with YOLOv10](https://mlsanity.com/paper/2603.03807/) (2026)
- [Why Domain Matters: A Preliminary Study of Domain Effects in Underwater Object Detection](https://mlsanity.com/paper/2604.26174/) (2026)
- [Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization](https://mlsanity.com/paper/2605.24716/) (2026)

## Abstract (arXiv)

Underwater object detection for robot picking has attracted a lot of interest. However, it is still an unsolved problem due to several challenges. We take steps towards making it more realistic by addressing the following challenges. Firstly, the currently available datasets basically lack the test set annotations, causing researchers must compare their method with other SOTAs on a self-divided test set (from the training set). Training other methods lead to an increase in workload and different researchers divide different datasets, resulting there is no unified benchmark to compare the performance of different algorithms. Secondly, these datasets also have other shortcomings, e.g., too many similar images or incomplete labels. Towards these challenges we introduce a dataset, Detecting Underwater Objects (DUO), and a corresponding benchmark, based on the collection and re-annotation of all relevant datasets. DUO contains a collection of diverse underwater images with more rational annotations. The corresponding benchmark provides indicators of both efficiency and accuracy of SOTAs (under the MMDtection framework) for academic research and industrial applications, where JETSON AGX XAVIER is used to assess detector speed to simulate the robot-embedded environment.