# The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation

arXiv: https://arxiv.org/abs/2108.06800 · HTML: https://mlsanity.com/paper/2108.06800/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 27 references that are in the map.
- First posted on arXiv 15 Aug 2021.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Fully Convolutional Networks for Semantic Segmentation](https://mlsanity.com/paper/1411.4038/) (2014)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Pyramid Scene Parsing Network](https://mlsanity.com/paper/1612.01105/) (2016)
- [Rethinking Atrous Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1706.05587/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [LinkNet: Exploiting Encoder Representations for Efficient Semantic Segmentation](https://mlsanity.com/paper/1707.03718/) (2017)
- [TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debris](https://mlsanity.com/paper/2007.08097/) (2020)
- [Deep Neural Networks for Marine Debris Detection in Sonar Images](https://mlsanity.com/paper/1905.05241/) (2019)

## Built on by (most cited first, 22 of 22)

- [CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Model](https://mlsanity.com/paper/2402.03631/) (2024)
- [Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Model](https://mlsanity.com/paper/2311.17112/) (2023)
- [WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmark](https://mlsanity.com/paper/2405.19818/) (2024)
- [A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detection](https://mlsanity.com/paper/2212.00352/) (2022)
- [When SAM Meets Sonar Images](https://mlsanity.com/paper/2306.14109/) (2023)
- [Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detection](https://mlsanity.com/paper/2203.07155/) (2022)
- [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)
- [The Marine Debris Forward-Looking Sonar Datasets](https://mlsanity.com/paper/2503.22880/) (2025)
- [Optimized Custom Dataset for Efficient Detection of Underwater Trash](https://mlsanity.com/paper/2305.16460/) (2023)
- [Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysis](https://mlsanity.com/paper/2405.18299/) (2024)
- [Sonar Image Datasets: A Comprehensive Survey of Resources, Challenges, and Applications](https://mlsanity.com/paper/2510.03353/) (2025)
- [USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robots](https://mlsanity.com/paper/2510.07869/) (2025)
- [RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labels](https://mlsanity.com/paper/2601.12715/) (2026)
- [CTFS : Collaborative Teacher Framework for Forward-Looking Sonar Image Semantic Segmentation with Extremely Limited Labels](https://mlsanity.com/paper/2603.21071/) (2026)
- [River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-index](https://mlsanity.com/paper/2207.06388/) (2022)
- [Histogram-based Parameter-efficient Tuning for Passive and Active Sonar Classification](https://mlsanity.com/paper/2504.15214/) (2025)
- [When Noise Meets Long-Tail: Feature-Threshold Dual Calibration for Robust Pseudo-Labeling](https://mlsanity.com/paper/2609.33668/) (2026)
- [SONIC: Sonar Image Correspondence using Pose Supervised Learning for Imaging Sonars](https://mlsanity.com/paper/2310.15023/) (2023)
- [Enhancing Marine Debris Acoustic Monitoring by Optical Flow-Based Motion Vector Analysis](https://mlsanity.com/paper/2412.20085/) (2024)
- [Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalization](https://mlsanity.com/paper/2605.24716/) (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)

Accurate detection and segmentation of marine debris is important for keeping the water bodies clean. This paper presents a novel dataset for marine debris segmentation collected using a Forward Looking Sonar (FLS). The dataset consists of 1868 FLS images captured using ARIS Explorer 3000 sensor. The objects used to produce this dataset contain typical house-hold marine debris and distractor marine objects (tires, hooks, valves,etc), divided in 11 classes plus a background class. Performance of state of the art semantic segmentation architectures with a variety of encoders have been analyzed on this dataset and presented as baseline results. Since the images are grayscale, no pretrained weights have been used. Comparisons are made using Intersection over Union (IoU). The best performing model is Unet with ResNet34 backbone at 0.7481 mIoU. The dataset is available at https://github.com/mvaldenegro/marine-debris-fls-datasets/