Research on Surface Defect Detection of Solar Pv Panels Based on Pre-Training Network and Feature Fusion

Abstract

Aiming at the problems of the lack of sample size and the complexity of defect images in defect detection task, based on the idea of transfer learning and hierarchical feature fusion, this paper proposes a deep classification network model of improved vgg19 pre-training network by analyzing the basic principle of feature extraction of convolutional neural network and getting inspiration from feature pyramid network. Then, the model is trained by the small-scale defect images of solar pv panel. Finally, the solar pv panel data set containing four kinds of defects, including cracks, debris, broken gates and black areas, is selected to comprehensively verify the effectiveness of the improved network in the defect detection task. The experimental results show that the proposed method is superior to the classical vgg19 network model in four evaluation indexes, such as accuracy, precision, recall rate and F1 score.