A Deep Two-Stage Scheme for Polycrystalline Micro-Crack Detection

Solar cell efficiency is one of the most concerned issues during the photovoltaic power generation. The existed micro-crack detection approaches mainly rely on manual work with the detection efficiency is low. Besides, there lacks exploration in algorithms for separating the region of damage without much workforce. In this paper, we propose a two-stage deep scheme, especially for the polycrystalline micro-crack detection method. A region of interest (ROI) proposal method based on the canny feature and the wavelet feature for micro-crack is raised. The computational efficiency and detection accuracy are greatly improved. Firstly, we split the original electroluminescent image (EL image) by its grid line and cut them into fixed-size squares. Then the ROI proposal method is applied to extract candidate boxes as the inputs of the second stage. At the second stage, a modified convolution neural network based on the candidate boxes is supervised by binary labels. Our experimental results demonstrate that our polycrystalline micro-crack detection scheme outperforms other traditional frameworks. This work is the first attempt to solve engineering problems about micro-crack by deep learning techniques in photovoltaic systems, without many high-quality labels and computing power.