An occlusion detection algorithm for small targets on the surface of photovoltaic modules based on deep learning

Solar energy is more and more widely used, but photovoltaic is also easily affected by environmental factors. Because of the remote location of the photovoltaic power station, it is very difficult to clean the surface cover manually. However, the use of robot cleaning depends on the accurate identification of the shielding objects. Due to the wide variety of shielding objects, the current technology is difficult to identify accurately. In view of the fact that the types of fallen leaves of photovoltaic panels are complex and difficult to clean, An occlusion detection algorithm for small targets on the surface of photovoltaic modules based on deep learning is proposed, and the model network method for quickly detecting leaf occlusion and determining the occlusion position of photovoltaic panels is discussed. In this paper, an improved YOLO-PX algorithm is proposed to identify and classify the occlusion of photovoltaic modules. Target detection experiments are carried out on the field data set of photovoltaic power station by using the original YOLO algorithm and the improved YOLO-PX algorithm. The experimental results show that the effect of the improved algorithm is good, and the detection accuracy and recall rate are 96.3% and 94.2% respectively. This method can provide technical support for the intelligent operation and maintenance of photovoltaic power station.