Ghost-RetinaNet: Fast Shadow Detection Method for Photovoltaic Panels Based on Improved RetinaNet

Abstract

Based on the artificial intelligence algorithm of RetinaNet, we propose the Ghost-RetinaNet in this paper, a fast shadow detection method for photovoltaic panels, to solve the problems of extreme target density, large overlap, high cost and poor real-time performance in photovoltaic panel shadow detection. Firstly, the Ghost CSP module based on Cross Stage Partial (CSP) is adopted in feature extraction network to improve the accuracy and detection speed. Based on extracted features, recursive feature fusion structure is mentioned to enhance the feature information of all objects. We introduce the SiLU activation function and CIoU Loss to increase the learning and generalization ability of the network and improve the positioning accuracy of the bounding box regression, respectively. Finally, in order to achieve fast detection, the Ghost strategy is chosen to lighten the size of the algorithm. The results of the experiment show that the average detection accuracy (mAP) of the algorithm can reach up to 97.17%, the model size is only 8.75 MB and the detection speed is highly up to 50.8 Frame per second (FPS), which can meet the requirements of real-time detection speed and accuracy of photovoltaic panels in the practical environment. The realization of the algorithm also provides new research methods and ideas for fault detection in the photovoltaic power generation system.