SEiPV-Net: An Efficient Deep Learning Framework for Autonomous Multi-Defect Segmentation in Electroluminescence Images of Solar Photovoltaic Modules

A robust and efficient segmentation framework is essential for accurately detecting and classifying various defects in electroluminescence images of solar PV modules. With the increasing global focus on renewable energy resources, solar PV energy systems are gaining significant attention. The inspection of PV modules throughout their manufacturing phase and lifespan requires an automatic and reliable framework to identify multiple micro-defects that are imperceptible to the human eye. This manuscript presents an encoder–decoder-based network architecture with the capability of autonomously segmenting 24 defects and features in electroluminescence images of solar photovoltaic modules. Certain micro-defects occupy a trivial number of image pixels, consequently leading to imbalanced classes. To address this matter, two types of class-weight assignment strategies are adopted, i.e., custom and equal class-weight assignments. The employment of custom class weights results in an increase in performance gains in comparison to equal class weights. Additionally, the proposed framework is evaluated by utilizing three different loss functions, i.e., the weighted cross-entropy, weighted squared Dice loss, and weighted Tanimoto loss. Moreover, a comparative analysis based on the model parameters is carried out with existing models to demonstrate the lightweight nature of the proposed framework. An ablation study is adopted in order to demonstrate the effectiveness of each individual block of the framework by carrying out seven different experiments in the study. Furthermore, SEiPV-Net is compared to three state-of-the-art techniques, namely DeepLabv3+, PSP-Net, and U-Net, in terms of several evaluation metrics, i.e., the mean intersection over union (IoU), F1 score, precision, recall, IoU, and Dice coefficient. The comparative and visual assessment using SOTA techniques demonstrates the superior performance of the proposed framework.