Robotic Detection of Marine Litter Using Deep Visual Detection Models

TL;DR

This study assesses deep learning models for underwater trash detection using AUVs, focusing on real-world datasets and evaluating performance across different hardware platforms for potential autonomous marine debris removal.

Contribution

It introduces a comprehensive evaluation of various deep-learning object detection models on a large underwater debris dataset, considering real-time processing constraints for AUV deployment.

Findings

Deep learning models can effectively detect underwater trash in realistic conditions.

Performance varies significantly across different hardware platforms.

The study provides insights into deploying AUVs for autonomous marine debris removal.

Abstract

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.