# IBURD: Image Blending for Underwater Robotic Detection

arXiv: https://arxiv.org/abs/2502.17706 · HTML: https://mlsanity.com/paper/2502.17706/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 43 references that are in the map.
- First posted on arXiv 24 Feb 2025.

## Builds on (most cited first, 22 of 22)

- [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)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Zero-Shot Text-to-Image Generation](https://mlsanity.com/paper/2102.12092/) (2021)
- [A Survey on Deep Learning in Medical Image Analysis](https://mlsanity.com/paper/1702.05747/) (2017)
- [YOLACT: Real-time Instance Segmentation](https://mlsanity.com/paper/1904.02689/) (2019)
- [Understanding Deep Image Representations by Inverting Them](https://mlsanity.com/paper/1412.0035/) (2014)
- [Neural Style Transfer: A Review](https://mlsanity.com/paper/1705.04058/) (2017)
- [A Survey of Modern Deep Learning based Object Detection Models](https://mlsanity.com/paper/2104.11892/) (2021)
- [GP-GAN: Towards Realistic High-Resolution Image Blending](https://mlsanity.com/paper/1703.07195/) (2017)
- [Deep Image Blending](https://mlsanity.com/paper/1910.11495/) (2019)
- [Synthesizing Training Data for Object Detection in Indoor Scenes](https://mlsanity.com/paper/1702.07836/) (2017)
- [Domain Adaptation for Object Detection via Style Consistency](https://mlsanity.com/paper/1911.10033/) (2019)
- [TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris](https://mlsanity.com/paper/2007.08097/) (2020)
- [Robotic Detection of Marine Litter Using Deep Visual Detection Models](https://mlsanity.com/paper/1804.01079/) (2018)
- [Improving Object Detection in Art Images Using Only Style Transfer](https://mlsanity.com/paper/2102.06529/) (2021)
- [Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer](https://mlsanity.com/paper/2002.01177/) (2020)
- [Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detection](https://mlsanity.com/paper/2203.07155/) (2022)
- [A Generative Approach Towards Improved Robotic Detection of Marine Litter](https://mlsanity.com/paper/1910.04754/) (2019)
- [HREyes: Design, Development, and Evaluation of a Novel Method for AUVs to Communicate Information and Gaze Direction](https://mlsanity.com/paper/2211.02946/) (2022)

## Built on by (most cited first, 1 of 1)

- [The Common Objects Underwater (COU) Dataset for Robust Underwater Object Detection](https://mlsanity.com/paper/2502.20651/) (2025)

## Abstract (arXiv)

We present an image blending pipeline, textitIBURD, that creates realistic synthetic images to assist in the training of deep detectors for use on underwater autonomous vehicles (AUVs) for marine debris detection tasks. Specifically, IBURD generates both images of underwater debris and their pixel-level annotations, using source images of debris objects, their annotations, and target background images of marine environments. With Poisson editing and style transfer techniques, IBURD is even able to robustly blend transparent objects into arbitrary backgrounds and automatically adjust the style of blended images using the blurriness metric of target background images. These generated images of marine debris in actual underwater backgrounds address the data scarcity and data variety problems faced by deep-learned vision algorithms in challenging underwater conditions, and can enable the use of AUVs for environmental cleanup missions. Both quantitative and robotic evaluations of IBURD demonstrate the efficacy of the proposed approach for robotic detection of marine debris.