This article presents a novel method for boundary extraction of photovoltaic (PV) plants using a fully convolutional network (FCN). Extracting the boundaries of PV plants is essential in the process of aerial inspection and autonomous monitoring by aerial robots. This method provides a clear delineation of the utility-scale PV plants’ boundaries for PV developers, operation and maintenance service providers for use in aerial photogrammetry, flight mapping, and path planning during the autonomous monitoring of PV plants. For this purpose, as a prerequisite, the “Amir” dataset consisting of aerial imagery of PV plants from different countries, has been collected. A Mask-RCNN architecture is employed as a deep network with VGG16 as a backbone to detect the boundaries precisely. As comparison, the results of another framework based on classical image processing are compared with the FCN performance in PV plants boundary detection. The results of the FCN demonstrate that the trained model is able to detect the boundaries of PV plants with an accuracy of 96.99% and site-specific tuning of boundary parameters is no longer required.

中文翻译：

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使用航空图像上的全卷积网络自动提取大型光伏电站边界


本文提出了一种使用全卷积网络 (FCN) 进行光伏 (PV) 发电厂边界提取的新方法。在空中机器人空中巡检和自主监控过程中，提取光伏电站边界至关重要。该方法为光伏开发商、运维服务提供商提供了清晰的公用事业规模光伏电站边界划分，用于光伏电站自主监控过程中的航空摄影测量、飞行测绘和路径规划。为此，作为先决条件，我们收集了由不同国家光伏电站航空图像组成的“Amir”数据集。采用 Mask-RCNN 架构作为深度网络，以 VGG16 为骨干来精确检测边界。作为比较，将另一个基于经典图像处理的框架的结果与FCN在光伏电站边界检测中的性能进行了比较。 FCN的结果表明，经过训练的模型能够以96.99%的准确度检测光伏电站的边界，并且不再需要针对特定​​地点调整边界参数。