Small-sized unmanned surface vehicles (USV) are coastal water devices with a broad range of applications such as environmental control and surveillance. A crucial capability for autonomous operation is obstacle detection for timely reaction and collision avoidance, which has been recently explored in the context of camera-based visual scene interpretation. Owing to curated datasets, substantial advances in scene interpretation have been made in a related field of unmanned ground vehicles. However, the current maritime datasets do not adequately capture the complexity of real-world USV scenes and the evaluation protocols are not standardised, which makes cross-paper comparison of different methods difficult and hinders the progress. To address these issues, we introduce a new obstacle detection benchmark MODS, which considers two major perception tasks: maritime object detection and the more general maritime obstacle segmentation. We present a new diverse maritime evaluation dataset containing approximately 81k stereo images synchronized with an on-board IMU, with over 60k objects annotated. We propose a new obstacle segmentation performance evaluation protocol that reflects the detection accuracy in a way meaningful for practical USV navigation. Nineteen recent state-of-the-art object detection and obstacle segmentation methods are evaluated using the proposed protocol, creating a benchmark to facilitate development of the field. The proposed dataset, as well as evaluation routines, are made publicly available at vicos.si/resources.
翻译:小型无人驾驶地面飞行器(USV)是具有环境控制和监视等广泛应用的沿海水装置。自主操作的关键能力是发现及时反应和避免碰撞的障碍,这是最近通过基于摄像的视觉场面判读而探索的。由于固化的数据集,在与无人驾驶地面飞行器有关的领域在现场判读方面取得了实质性进展。然而,目前的海洋数据集没有充分捕捉到真实世界的USV场景的复杂性,评价协议没有标准化,这使得不同方法的跨纸比较难以进行,阻碍了进展。为了解决这些问题,我们引入了新的障碍探测基准MODS, 认为有两个主要的认知任务:海洋物体探测和更普遍的海洋障碍分割。我们提出了一个新的不同的海洋评价数据集,其中包括与机上无人驾驶地面飞行器同步的大约81公里立式图像,有60公里以上的物体附加说明。我们提出了一个新的障碍分解性绩效评估协议,反映了探测准确性,对实际的USV导航具有意义。19个最新状态的物体探测和障碍分解方法难以进行比较,并阻碍取得进展。我们提出了新的障碍检测基准,正在使用拟议的常规评估,作为实地评估的基础。