ENHANCE PV PANEL DETECTION USING DRONE EQUIPPED WITH RTK

摘要

Solar energy is getting a lot of traction due to the reduced cost and friendlier to the environment compared to fossil fuel. It is essential to inspect the PV farms to ensure that the correct capacity produced through early PV fault detection. We proposed a full autonomous solution, where the drone mission is programmed to follow a specific Global Positioning System (GPS) waypoints. The collected videos will undergo various image processing techniques to detect and track the PV panels. In this paper, we tried two different PV panel detection approaches. Both detections gave acceptable results. The first detection relies on various image processing techniques. The second detection relies on deep learning architecture called mask Region-based Convolution Neural Network (R-CNN). After that, we track the PV panels in every frame using camera data alone. The advantage of tracking the P V panels is to ensure unrepeated PV panel through tagging even if the drone flies over the panel again since each PV panel will be associated with a tag. The next step will be to test the PV panel's proposed detection and tracking algorithm on a larger solar farm.