mlsanityData to 7 Oct 2026

The Marine Debris Dataset for Forward-Looking Sonar Semantic Segmentation

arXiv 2108.06800 ↗First posted 15 Aug 2021cs.CV cs.ROcited by 22

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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/

Key facts. Cited by 22 papers in the map (data through 7 Oct 2026). Builds on 14 of its 27 references that are in the map. First posted on arXiv 15 Aug 2021.

Builds on

14 of 27 references.

References · 14

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2 more
  1. 2020TrashCan: A Semantically-Segmented Dataset towards Visual Detection of Marine Debriscited by 45
  2. 2019Deep Neural Networks for Marine Debris Detection in Sonar Imagescited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2022: 3; 2023: 4; 2024: 6; 2025: 4; 2026: 5.

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  1. 2024CAT-SAM: Conditional Tuning for Few-Shot Adaptation of Segment Anything Modelcited by 27
  2. 2023Parameter Efficient Fine-tuning via Cross Block Orchestration for Segment Anything Modelcited by 15
  3. 2024WebUOT-1M: Advancing Deep Underwater Object Tracking with A Million-Scale Benchmarkcited by 15
  4. 2022A Dataset with Multibeam Forward-Looking Sonar for Underwater Object Detectioncited by 11
  5. 2023When SAM Meets Sonar Imagescited by 10
  6. 2022Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detectioncited by 9
  7. 2024SeafloorAI: A Large-scale Vision-Language Dataset for Seafloor Geological Surveycited by 6
  8. 2024Sonar-based Deep Learning in Underwater Robotics: Overview, Robustness and Challengescited by 5
  9. 2025The Marine Debris Forward-Looking Sonar Datasetscited by 4
  10. 2023Optimized Custom Dataset for Efficient Detection of Underwater Trashcited by 3
  11. 2024Deep Learning Innovations for Underwater Waste Detection: An In-Depth Analysiscited by 3
  12. 2025Sonar Image Datasets: A Comprehensive Survey of Resources, Challenges, and Applicationscited by 2
10 more
  1. 2025USIM and U0: A Vision-Language-Action Dataset and Model for General Underwater Robotscited by 2
  2. 2026RSOD: Reliability-Guided Sonar Image Object Detection with Extremely Limited Labelscited by 2
  3. 2022River Surface Patch-wise Detector Using Mixture Augmentation for Scum-cover-indexcited by 1
  4. 2025Histogram-based Parameter-efficient Tuning for Passive and Active Sonar Classificationcited by 1
  5. 2026CTFS : Collaborative Teacher Framework for Forward-Looking Sonar Image Semantic Segmentation with Extremely Limited Labelscited by 1
  6. 2023SONIC: Sonar Image Correspondence using Pose Supervised Learning for Imaging Sonarscited by 0
  7. 2024Enhancing Marine Debris Acoustic Monitoring by Optical Flow-Based Motion Vector Analysiscited by 0
  8. 2026Physics-Guided Self-Supervised Statistical Residual Learning for Sonar Despeckling with Improved Generalizationcited by 0
  9. 2026Beyond Pixel Similarity: Task-Aware Evaluation of GAN-Based Synthetic Sonar Data for Robotic Perceptioncited by 0
  10. 2026When Noise Meets Long-Tail: Feature-Threshold Dual Calibration for Robust Pseudo-Labelingcited by 0