High-resolution Photoelectric Imaging Enhanced with Artificial Intelligence for Early Detection of Contact Defects Within Solar Cells

Maryam Jawad Kadhim

doi:10.26706/ijceae.7.3.20260314

Abstract

The aim of this research is the development of an intelligent system based on artificial intelligence for early defect detection of solar cells by applying high-resolution photoelectric imaging technique. To analyze images and extract patterns that might be used to determine defects in solar cells, an integrated methodology was designed that combines digital image processing and deep learning algorithms, specifically convolutional neural networks (CNNs). The proposed system utilizes a comprehensive database containing healthy and defective solar cell images. The database is processed through image enhancement and normalization techniques to improve prediction accuracy and model reliability. Experimental results demonstrated that the proposed model achieved detection accuracy ranging from 96.4% to 97.8%, sensitivity of 96.5%, and discriminatory accuracy of 98.2%, confirming its capability to identify different defect types including micro-cracks, discontinuities, and non-uniform reflection regions. ROC curve analysis further validated the stability and generalization capability of the system on unseen test samples. The generated thermal maps provide accurate visual representation of defects, reducing human inspection errors by more than 80% compared with conventional infrared imaging approaches. The proposed AI-based framework demonstrates strong potential for industrial-scale, real-time, and non-destructive inspection of photovoltaic cells, supporting intelligent monitoring systems for sustainable solar energy applications.

Keywords

Photoelectric imaging Artificial intelligence Solar cell defects Convolutional neural networks (CNNs) Image analysis Early detection Digital image processing Solar energy Non-destructive testing Thermal mapping

References

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[3] W. Liu, “Development of 2D Material Conformal Coating Technology for Advanced Photonics Devices,” Ph.D. dissertation, RMIT University, 2024.

[4] A. Machín and F. Márquez, “Next-generation chemical sensors: The convergence of nanomaterials, advanced characterization, and real-world applications,” Chemosensors, vol. 13, no. 9, p. 345, 2025.

[5] S. A. Serroune, I. A. Harijoko, I. Khasani, M. Estrada, and I. Christos, “Geocasing-embedded AI monitoring systems for enhanced quantum geothermal operations in volcanic environments: Multi-lithological response analysis and predictive seismic intelligence,” 2025.

[6] A. K. Srivastav and P. Das, Nanobiotechnology: AI and IoT Applications and Emerging Implications. Singapore: Springer Nature, 2025.

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[14] Q. Wu, Z. Tang, C. Chen, H. Huang, D. Yu, L. Shi, et al., “Dual-bionic titanium scaffolds based on a gyroid-sheet structure and BaTiO₃ piezoelectric coating: A synergistic approach for bone defect repair,” Materials Futures, vol. 4, no. 4, Art. no. 045401, 2025.

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