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

2025-02-28

AI Summary

Existing underwater datasets suffer from severe underrepresentation of artificial object categories and insufficient multi-scene diversity required for real-time AUV perception, hindering lightweight model training. To address this, we introduce COU—the first large-scale, instance-segmentation-focused dataset dedicated to underwater artificial objects—comprising ~10,000 images across pool, lake, and marine environments, annotated with high-fidelity instance masks for 24 AUV-relevant classes (e.g., marine debris, diving gear, AUV hulls). COU systematically bridges critical gaps in artificial object coverage and environmental variability, overcoming the biological bias and engineering applicability limitations of prior datasets. We conduct cross-domain transfer learning and benchmarking using Mask R-CNN, YOLOv8, and RT-DETR, demonstrating that COU-trained models achieve significantly higher accuracy and inference speed than those trained on terrestrial datasets. Results validate the essential role of domain-specific underwater annotation data in enhancing AUV perception robustness. The dataset is publicly released.

Technology Category

• Computer Vision: Object Detection & Categorization
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Humans and AI: Crowd Sourcing and Human Computation

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search

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.

Problem

• Addresses lack of underwater instance segmentation datasets.
• Provides diverse object classes beyond marine life.
• Enhances training for real-time AUV object detectors.

Innovation

• COU dataset for underwater instance segmentation
• Diverse object classes in aquatic environments
• Training lightweight real-time AUV detectors