2020/10/15 by Evgenii Sovetkin, Elbert Jan Achterberg, Sovetkin, Evgenii +5 · 1 citation
Computer Science · Energy · Engineering · #Artificial intelligence #Artificial neural network #Computer Vision and Pattern Recognition (cs.CV) #Computer science #Computer vision #Encoder #Engineering #FOS: Computer and information sciences #FOS: Electrical engineering #Feature (linguistics) #Image and Video Processing (eess.IV) #Image segmentation #Industrial Vision Systems and Defect Detection #Pattern recognition (psychology) #Photovoltaic System Optimization Techniques #Photovoltaic system #Sample (material) #Segmentation #cs.CV #eess.IV #electronic engineering #information engineering
paper · pdf · doi:10.48550/arxiv.2010.07556
arxiv created 2020/10/15 · openalex publication_date 2020/10/15 · arxiv updated 2020/10/16 · openalex created_date 2022/07/25 · openalex updated_date 2026/07/28
We consider a series of image segmentation methods based on the deep neural networks in order to perform semantic segmentation of electroluminescence (EL) images of thin-film modules. We utilize the encoder-decoder deep neural network architecture. The framework is general such that it can easily be extended to other types of images (e.g. thermography) or solar cell technologies (e.g. crystalline silicon modules). The networks are trained and tested on a sample of images from a database with 6000 EL images of Copper Indium Gallium Diselenide (CIGS) thin film modules. We selected two types of features to extract, shunts and so called "droplets". The latter feature is often observed in the set of images. Several models are tested using various combinations of encoder-decoder layers, and a procedure is proposed to select the best model. We show exemplary results with the best selected model. Furthermore, we applied the best model to the full set of 6000 images and demonstrate that the automated segmentation of EL images can reveal many subtle features which cannot be inferred from studying a small sample of images. We believe these features can contribute to process optimization and quality control.