태양광 모듈 이미지 결함 검출 모델의 미세 조정 및 데이터셋 최적화에 관한 연구

• Published in: 한국태양에너지학회 논문집, 45(4) pp. 67 - 79
• Main Authors: 김태윤, 여병철
• Format: Journal Article
• Language: Korean
• Published: 한국태양에너지학회 01.08.2025
• Subjects: 기타공학
• ISSN: 1598-6411; 2508-3562

Fault diagnosis of photovoltaic (PV) modules is essential for reliable solar energy generation. Previously, defects in solar modules were identified through manual inspection of PV module images; however, these methods are inefficient and prone to errors. To address this issue, deep-learning-based approaches have been explored to automate defect diagnosis in solar modules. Nevertheless, there remains a lack of systematic analysis on how model structure optimization and dataset composition affect classification performance. This study investigates the effectiveness of layer-wise fine-tuning and dataset integration using image classification models retrained on infrared (IR) and electroluminescence (EL) images. Specifically, we compared three AlexNet-based training strategies: (1) retraining AlexNet from scratch using only IR images, (2) fine-tuning a model pretrained on general image datasets with IR images, and (3) fine-tuning a model pretrained on general image datasets using both IR and EL images. The results indicated that the best performance was achieved by freezing the first three convolutional layers during fine-tuning, with the AlexNet model fine-tuned on the IR dataset achieving the highest classification accuracy. These findings suggest that combining fine-tuned models with selective layer freezing and tailored datasets can effectively enhance the design of defect detection systems for photovoltaic module diagnostics. KCI Citation Count: 0