# ResAttUNet: Detecting Marine Debris using an Attention activated Residual UNet

arXiv: https://arxiv.org/abs/2210.08506 · HTML: https://mlsanity.com/paper/2210.08506/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 32 references that are in the map.
- First posted on arXiv 16 Oct 2022.

## Builds on (most cited first, 4 of 4)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [CBAM: Convolutional Block Attention Module](https://mlsanity.com/paper/1807.06521/) (2018)
- [High-precision Density Mapping of Marine Debris and Floating Plastics via Satellite Imagery](https://mlsanity.com/paper/2210.05468/) (2022)

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

- [Assessment of Spectral based Solutions for the Detection of Floating Marine Debris](https://mlsanity.com/paper/2408.10187/) (2024)

## Abstract (arXiv)

Currently, a significant amount of research has been done in field of Remote Sensing with the use of deep learning techniques. The introduction of Marine Debris Archive (MARIDA), an open-source dataset with benchmark results, for marine debris detection opened new pathways to use deep learning techniques for the task of debris detection and segmentation. This paper introduces a novel attention based segmentation technique that outperforms the existing state-of-the-art results introduced with MARIDA. The paper presents a novel spatial aware encoder and decoder architecture to maintain the contextual information and structure of sparse ground truth patches present in the images. The attained results are expected to pave the path for further research involving deep learning using remote sensing images. The code is available at https://github.com/sheikhazhanmohammed/SADMA.git