This paper presents SubPipe, an underwater dataset for SLAM, object detection, and image segmentation. SubPipe has been recorded using a \gls{LAUV}, operated by OceanScan MST, and carrying a sensor suite including two cameras, a side-scan sonar, and an inertial navigation system, among other sensors. The AUV has been deployed in a pipeline inspection environment with a submarine pipe partially covered by sand. The AUV's pose ground truth is estimated from the navigation sensors. The side-scan sonar and RGB images include object detection and segmentation annotations, respectively. State-of-the-art segmentation, object detection, and SLAM methods are benchmarked on SubPipe to demonstrate the dataset's challenges and opportunities for leveraging computer vision algorithms. To the authors' knowledge, this is the first annotated underwater dataset providing a real pipeline inspection scenario. The dataset and experiments are publicly available online at https://github.com/remaro-network/SubPipe-dataset

翻译：本文介绍SubPipe——一个面向SLAM、目标检测及图像分割的水下数据集。SubPipe由OceanScan MST公司操作的LAUV采集，搭载包含两台相机、侧扫声呐及惯性导航系统等的传感器套件。自主水下航行器（AUV）部署在部分管道被沙掩埋的海底管道检测环境中，其位姿真值通过导航传感器估计获得。侧扫声呐图像配备目标检测标注，RGB图像则包含分割标注。我们基于SubPipe对当前最先进的分割、目标检测及SLAM方法进行了基准测试，以展示数据集面临的挑战以及利用计算机视觉算法的机遇。据作者所知，这是首个提供真实管道检测场景的带标注水下数据集。数据集及实验代码已发布于https://github.com/remaro-network/SubPipe-dataset。