A Review on Defect Detection of Electroluminescence-Based Photovoltaic Cell Surface Images Using Computer Vision

Abstract

The past two decades have seen an increase in the deployment of photovoltaic installations as nations around the world try to play their part in dampening the impacts of global warming. The manufacturing of solar cells can be defined as a rigorous process starting with silicon extraction. The increase in demand has multiple implications for manual quality inspection. With automated inspection as the ultimate goal, researchers are actively experimenting with convolutional neural network architectures. This review presents an overview of the electroluminescence image-extraction process, conventional image-processing techniques deployed for solar cell defect detection, arising challenges, the present landscape shifting towards computer vision architectures, and emerging trends.

Automatic Classification of Defective Solar Panels in Electroluminescence Images Based on Random Connection Network

“…Along with open-source deep learning software frameworks (TensorFlow 2.6.2 [14] and PyTorch 2.0.0 [15]), an explosion in the availability of high-performance computing infrastructures (especially GPUs [16]), new network architectures [17], and training techniques [18] means that neural networks can be trained much deeper. The latest advances in deep learning techniques based on convolutional neural networks have led to significant progress in object localization and recognition under natural conditions, such as regions with convolutional neural network (R-CNN) features [19], spatial pyramid pooling networks [20], fast region convolutional neural networks [21], faster region CNN [22], single shot multibox detectors [23], you only look once [24], region-based fully convolutional networks (FCN) [25], and other extended variants of networks [26,27]. For example, an intelligent algorithm utilizing both a high-resolution network (HRNet) and self-fusion network (SeFNet) for automatic defect identification of PV modules using EL images has been proposed, demonstrating enhanced feature fusion and classification accuracy for superior defect recognition performance [28].…”