ZHANG Jiening, OUYANG Sen, KANG Lan, GUO Yifan, ZHANG Jinming
Under the development trend of distributed PV with high penetration rate into the distribution network, there is a real and urgent need to accurately recognize the area of roofs where PV can be constructed. In response to the demand for further identification processing for the lack of accurate roof area identification and the influence of roof foreign objects on the construction of PV power supply, this paper proposes a method for extracting the effective area of the roof of a building by integrating the image classification mechanism. First of all, this paper takes into account the influence of foreign objects on the effective roof area, and makes a new classification of different types of building roofs according to the geometric characteristics of the roof, edge characteristics, and the degree of utilization of the internal area, so as to reduce the influence of the complexity of the roof on the extraction accuracy. Then, this paper adopts image classification network and improved Mask-RCNN network to realize the classification and extraction of building roofs. Among them, increasing the image classification network reduces the influence of multi-type roofs on the learning ability of the network, and improving the Mask-RCNN network is based on the original network, introducing the attention mechanism module and optimizing the FPN network to improve the feature learning and extraction ability of the network. Finally, based on the constructed image sample library of roofs of single buildings, the effectiveness and accuracy of the method proposed in this paper are verified.
