Deeplab V3+ based segmentation method for PV panels with aerial orthoimages

Abstract

The health management and maintenance of photovoltaic (PV) plants are inherent problems in the PV industry. The need to establish digital positioning for each PV string and PV module is urgent. This paper provides a complete end-to-end system for digital segmentation and localization of PV strings and modules on the aerial orthophotos. The system includes three main parts: (1) the dataset built from the images captured by Unmanned Aerial Vehicles (UAV) and corresponding image preprocessing techniques. (2) a modified Deeplab V3+ neural network is designed to extract the PV strings in the aerial orthophotos. (3) a PV module extraction algorithm is introduced to get the centroid of every PV module and the sliding window strategy is adopted to avoid the chopped PV strings problem. With the above process, the digital location information of PV panels can be correlated with the actual physical information. We conduct detailed experiments with actual scene data and different models. The extensive results confirm the accuracy and efficiency of the system proposed in this paper with comparative analysis.