A Multi-Stage Ensemble Deep Learning Framework for Crack Segmentation and Feature-Based Power Loss Projection from Electroluminescence Images of Photovoltaic Cells

• Proposed a novel Multi-Stage Ensemble Deep Learning Framework for Crack Segmentation and Feature-Based Power Loss Projection (MSC-FPL) using EL images. • Developed an Ensemble-based Binary Classification (EBC) CNN model to classify EL images of PV cells into two classes (Normal and Defective) in the first stage of the proposed MSC-FPL. • Developed an Ensemble-based Multi-Class Semantic Segmentation (EMSS) CNN model in the second stage of the proposed MSC-FPL to perform pixel-level classification of the defective class obtained from the first stage into six classes: Cross, Diagonal cracks relative to the busbar, Parallel cracks relative to the busbar, Perpendicular cracks relative to the busbar, Multiple Direction cracks, and Busbars as a key feature of the PV cell. • Introduced a novel feature-based approach that integrates cell-level crack features, specifically orientation and size, into a structured power loss projection framework. • Quantitative and qualitative results were presented for each stage to validate the proposed framework. Crack detection in Photovoltaic (PV) cells using Electroluminescence (EL) imaging has become a primary research focus due to cracks being one of the most common defects that cause power loss from PV modules. Moreover, the extent of power output loss differs based on the size and orientation of the cracks. Existing deep learning approaches exhibit low predictive performance and offer limited analysis of segmented crack features. To the best of our knowledge, no previous deep learning–based study has considered the correlation between cell-level crack features and feature-based power loss projection using quantitative measurements. Therefore, we proposed a novel Multi-Stage Ensemble Deep Learning Framework for Crack Segmentation and Feature-Based Power Loss Projection in PV Cells (MSC-FPL) to address the aforementioned limitations. In Stage 1, we developed an Ensemble-based Binary Classification (EBC) CNN model that achieved an accuracy of 98.19%. The output of Stage 1 is used as input for Stage 2, where an Ensemble-based Multi-Class Semantic Segmentation (EMSS) CNN model achieved a Mean Intersection over Union (mIoU) of 85.01%. The subsequent stages utilize the output from Stage 2, which provides crack segmentation based on orientation. In Stage 3, crack size features are calculated, and in Stage 4, these features are integrated within a feature-based power loss projection framework using a novel interaction-based formulation. Our results confirm the effectiveness of the proposed framework through comparison with existing approaches, improve PV module quality assessment, and provide an intelligent support system for PV module monitoring, contributing to the sustainability of solar energy systems.