The Common Objects Underwater (COU) Dataset for Robust Underwater Object Detection

TL;DR

The COU dataset provides a comprehensive collection of underwater images with instance segmentation annotations, enabling improved training of real-time underwater object detectors for diverse aquatic environments.

Contribution

This paper introduces the COU dataset, the first large-scale underwater object dataset with diverse classes and environments, tailored for training robust underwater detection models.

Findings

COU-trained detectors outperform terrestrial-trained models in underwater scenarios.

The dataset covers 24 object classes across various aquatic environments.

Evaluation shows improved accuracy and efficiency of models trained on COU.

Abstract

We introduce COU: Common Objects Underwater, an instance-segmented image dataset of commonly found man-made objects in multiple aquatic and marine environments. COU contains approximately 10K segmented images, annotated from images collected during a number of underwater robot field trials in diverse locations. COU has been created to address the lack of datasets with robust class coverage curated for underwater instance segmentation, which is particularly useful for training light-weight, real-time capable detectors for Autonomous Underwater Vehicles (AUVs). In addition, COU addresses the lack of diversity in object classes since the commonly available underwater image datasets focus only on marine life. Currently, COU contains images from both closed-water (pool) and open-water (lakes and oceans) environments, of 24 different classes of objects including marine debris, dive tools, and AUVs. To assess the efficacy of COU in training underwater object detectors, we use three state-of-the-art models to evaluate its performance and accuracy, using a combination of standard accuracy and efficiency metrics. The improved performance of COU-trained detectors over those solely trained on terrestrial data demonstrates the clear advantage of training with annotated underwater images. We make COU available for broad use under open-source licenses.