Performance Analysis of Classification and Detection for PV Panel Motion Blur Images Based on Deblurring and Deep Learning Techniques

Abstract

Detecting snow-covered solar panels is crucial as it allows us to remove snow using heating techniques more efficiently and restores the photovoltaic system to proper operation. This paper presents classification and detection performance analyses for snow-covered solar panel images. The classification analysis consists of two cases, and the detection analysis consists of one case based on three backbones. In this study, five deep learning models, namely visual geometry group-16 (VGG-16), VGG-19, residual neural network-18 (RESNET-18), RESNET-50, and RESNET-101, are used to classify solar panel images. The models are trained, validated, and tested under different conditions. The first case of classification is performed on the original dataset without preprocessing. In the second case, extreme climate conditions are simulated by generating motion noise; furthermore, the dataset is replicated using the upsampling technique to handle the unbalancing issue. For the detection case, a region-based convolutional neural network (RCNN) detector is used to detect the three categories of solar panels, which are all_snow, no_snow, and partial. The dataset of these categories is taken from the second case in the classification approach. Finally, we proposed a blind image deblurring algorithm (BIDA) that can be a preprocessing step before the CNN (BIDA-CNN) model. The accuracy of the models was compared and verified; the accuracy results show that the proposed CNN-based blind image deblurring algorithm (BIDA-CNN) outperformed other models evaluated in this study.