The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition

Robust object detection is crucial for automating underwater marine debris collection. While supervised deep learning achieves state-of-the-art performance in discriminative tasks, replicating this success on underwater data is challenging. The generalization of these methods suffers due to a lack of available annotated data considering different sources of variation in the unstructured underwater environment and imaging conditions. In this paper, we present the Seaclear Marine Debris Dataset, the first publicly available shallow-water marine debris dataset annotated for instance segmentation/object detection. The dataset contains 8610 images collected using ROVs at multiple locations and with different cameras, annotated for 40 object categories, encompassing not only litter but also observed animals, plants, and robot parts. As part of the technical validation, we provide baseline results for object detection using Faster RCNN and YOLOv6 models. Furthermore, we demonstrate the non-triviality of generalizing the trained model performance to unseen sites and cameras due to domain shift. This underscores the value of the presented dataset in further developing robust models for underwater debris detection.

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Acoustic Camera-Based Automatic Abundance Estimation Algorithm for Marine Plastic Debris

Zhou,

Mizuno

2024

OCEANS 2024 - Halifax

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The CleanSea Set: A Benchmark Corpus for Underwater Debris Detection and Recognition

Cited by 3 publications

References 14 publications

Applications of convolutional neural networks for intelligent waste identification and recycling: A review

Applications of convolutional neural networks for intelligent waste identification and recycling: A review

A Dataset for Detection and Segmentation of Underwater Marine Debris in Shallow Waters

Acoustic Camera-Based Automatic Abundance Estimation Algorithm for Marine Plastic Debris

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