COU: Common Objects Underwater

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 aquatic 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 the availability of annotated underwater images.

我们提出COU（Common Objects Underwater，水下常见物体）：一款面向多种水生与海洋环境中常见人造物体的实例分割图像数据集。该数据集包含约1万张带标注的分割图像，其标注素材来自多场不同地点开展的水下机器人实地试验所采集的原始图像。构建COU的初衷，是为了解决当前水下实例分割任务缺乏经过精心遴选、具备全面类别覆盖度的数据集的问题，该数据集尤其适用于为自主水下航行器（Autonomous Underwater Vehicles，AUV）训练轻量化、可实时运行的目标检测器。此外，现有主流水生图像数据集仅聚焦海洋生物，存在物体类别多样性不足的短板，而COU则有效弥补了这一缺陷。目前，COU涵盖封闭水环境（泳池）与开放水环境（湖泊与海洋）中的图像，共包含24类不同物体，涵盖海洋垃圾、潜水工具以及自主水下航行器等类别。为评估COU在水下目标检测器训练中的应用效能，我们采用三类当前最优模型，结合标准精度与效率指标，对其性能与准确度展开了评估。相较于仅使用陆地数据训练的检测器，经COU训练的检测器性能更优异，这充分证明了带标注水下图像的显著应用优势。

搜集汇总

背景与挑战

背景概述

COU: Common Objects Underwater是一个实例分割图像数据集，专注于水下环境中常见的人造物体，包含约10K分割图像，覆盖24个类别如海洋垃圾、潜水工具和AUVs。该数据集旨在解决水下实例分割数据集的缺乏，支持自主水下航行器（AUVs）的轻量实时检测器训练，并包括封闭和开放水域环境，以提升检测性能。