Laboratoire de technologie écologique (ECOL), Institut d'ingénierie de l'environnement (IIE), Faculté de l'environnement naturel, architectural et construit (ENAC), Ecole polytechnique fédérale de Lausanne (EPFL), 1015 Lausanne, Switzerland.
PLoS One. 2018 Oct 22;13(10):e0205319. doi: 10.1371/journal.pone.0205319. eCollection 2018.
We describe and test an obstacle-detection system for small, lake-deployed autonomous surface vehicles (ASVs) that relies on a low-cost, consumer-grade camera and runs on a single-board computer. A key feature of lakes that must be accounted for is the frequent presence of the shoreline in images as well as the land-sky boundary. These particularities, along with variable weather conditions, result in a wide range of scene variations, including the possible presence of glint. The implemented algorithm is based on two main steps. First, possible obstacles are detected using an innovative gradient-based image processing algorithm developed especially for a camera with a low viewing angle to the water (i.e., the situation for a small ASV). Then, true and false positives are differentiated using correlation-based multi-frame analysis. The algorithm was tested extensively on a small ASV deployed in Lake Geneva. Under operational conditions, the algorithm processed 640×480-pixel images from a Raspberry Pi Camera at about 3-4 Hz on a Raspberry Pi 3 Model B computer. The present algorithm demonstrates that single-board computers can be used for effective and low-cost obstacle detection systems for ASVs operating in variable lake conditions.
我们描述并测试了一种适用于小型湖泊自主水面车辆（ASV）的障碍物检测系统，该系统依赖于低成本的消费级相机，并在单板计算机上运行。湖泊的一个必须考虑的关键特征是，在图像中经常出现海岸线以及地空边界。这些特殊性，加上多变的天气条件，导致了广泛的场景变化，包括可能存在的反光。所实现的算法基于两个主要步骤。首先，使用专门为低视角相机（即小型 ASV 的情况）开发的创新基于梯度的图像处理算法来检测可能的障碍物。然后，使用基于相关的多帧分析来区分真假阳性。该算法在日内瓦湖上部署的小型 ASV 上进行了广泛测试。在操作条件下，该算法以约 3-4 Hz 的速度处理来自 Raspberry Pi 相机的 640×480 像素图像，该算法在 Raspberry Pi 3 Model B 计算机上运行。目前的算法表明，单板计算机可用于在多变的湖泊条件下运行的 ASV 的有效且低成本的障碍物检测系统。
