# Robotic Detection of Marine Litter Using Deep Visual Detection Models

arXiv: https://arxiv.org/abs/1804.01079 · HTML: https://mlsanity.com/paper/1804.01079/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2018.

## Built on by (most cited first, 24 of 24)

- [Towards More Efficient EfficientDets and Low-Light Real-Time Marine Debris Detection](https://mlsanity.com/paper/2203.07155/) (2022)
- [Waste detection in Pomerania: non-profit project for detecting waste in environment](https://mlsanity.com/paper/2105.06808/) (2021)
- [AquaticCLIP: A Vision-Language Foundation Model for Underwater Scene Analysis](https://mlsanity.com/paper/2502.01785/) (2025)
- [MarineDet: Towards Open-Marine Object Detection](https://mlsanity.com/paper/2310.01931/) (2023)
- [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)
- [Robotic Detection of a Human-Comprehensible Gestural Language for Underwater Multi-Human-Robot Collaboration](https://mlsanity.com/paper/2207.05331/) (2022)
- [The Common Objects Underwater (COU) Dataset for Robust Underwater Object Detection](https://mlsanity.com/paper/2502.20651/) (2025)
- [A Robotic Approach towards Quantifying Epipelagic Bound Plastic Using Deep Visual Models](https://mlsanity.com/paper/2105.01882/) (2021)
- [IBURD: Image Blending for Underwater Robotic Detection](https://mlsanity.com/paper/2502.17706/) (2025)
- [StreetView-Waste: A Multi-Task Dataset for Urban Waste Management](https://mlsanity.com/paper/2511.16440/) (2025)
- [Is the use of Deep Learning and Artificial Intelligence an appropriate means to locate debris in the ocean without harming aquatic wildlife?](https://mlsanity.com/paper/2112.00190/) (2021)
- [Transfer Learning for Instance Segmentation of Waste Bottles using Mask R-CNN Algorithm](https://mlsanity.com/paper/2204.07437/) (2022)
- [GAMMA: Generative Augmentation for Attentive Marine Debris Detection](https://mlsanity.com/paper/2212.03759/) (2022)
- [Visual Material Characteristics Learning for Circular Healthcare](https://mlsanity.com/paper/2309.04763/) (2023)
- [Intelligent Debris Mass Estimation Model for Autonomous Underwater Vehicle](https://mlsanity.com/paper/2309.10617/) (2023)
- [Adaptive Landmark Color for AUV Docking in Visually Dynamic Environments](https://mlsanity.com/paper/2310.02944/) (2023)
- [Cognitive Edge Device (CED) for Real-Time Environmental Monitoring in Aquatic Ecosystems](https://mlsanity.com/paper/2401.06157/) (2023)
- [Garbage Segmentation and Attribute Analysis by Robotic Dogs](https://mlsanity.com/paper/2404.18112/) (2024)
- [Towards the in-situ Trunk Identification and Length Measurement of Sea Cucumbers via B'ezier Curve Modelling](https://mlsanity.com/paper/2406.13951/) (2024)
- [Utilisation of Vision Systems and Digital Twin for Maintaining Cleanliness in Public Spaces](https://mlsanity.com/paper/2411.05964/) (2024)
- [RoLID-11K: A Dashcam Dataset for Small-Object Roadside Litter Detection](https://mlsanity.com/paper/2601.00398/) (2026)
- [The Garbage Dataset (GD): A Multi-Class Image Benchmark for Automated Waste Segregation](https://mlsanity.com/paper/2602.10500/) (2026)
- [Empowering Semantic-Sensitive Underwater Image Enhancement with VLM](https://mlsanity.com/paper/2603.12773/) (2026)

## Abstract (arXiv)

Trash deposits in aquatic environments have a destructive effect on marine ecosystems and pose a long-term economic and environmental threat. Autonomous underwater vehicles (AUVs) could very well contribute to the solution of this problem by finding and eventually removing trash. This paper evaluates a number of deep-learning algorithms preforming the task of visually detecting trash in realistic underwater environments, with the eventual goal of exploration, mapping, and extraction of such debris by using AUVs. A large and publicly-available dataset of actual debris in open-water locations is annotated for training a number of convolutional neural network architectures for object detection. The trained networks are then evaluated on a set of images from other portions of that dataset, providing insight into approaches for developing the detection capabilities of an AUV for underwater trash removal. In addition, the evaluation is performed on three different platforms of varying processing power, which serves to assess these algorithms' fitness for real-time applications.