研究目的
To develop a deep learning-based automatic defect identification method for photovoltaic modules using electroluminescence images, addressing the challenges of limited EL image samples and efficient defect classification.
研究成果
The proposed deep learning-based solution effectively addresses the challenges of limited EL image samples and efficient defect classification, demonstrating high accuracy in defect detection. Future work includes expanding the range of detectable defects and developing lightweight models for UAV-based inspection.
研究不足
The study acknowledges the limitations of overfitting with conventional image processing methods and the computational complexity and time required for GAN-based image generation. The robustness of the models in detecting multiple coexisting defects and the need for lightweight models for UAV deployment are noted as areas for future improvement.
1：Experimental Design and Method Selection:
The study combines traditional image processing technology with Generative Adversarial Network (GAN) characteristics for EL image generation and uses a Convolutional Neural Network (CNN) for defect classification.
2：Sample Selection and Data Sources:
The dataset consists of EL images from both public domain and private datasets, including 1800 EL images categorized into defect-free, micro-crack, break, and finger-interruption.
3：List of Experimental Equipment and Materials:
The study utilizes NVIDIA TITAN GPUs for model training and testing.
4：Experimental Procedures and Operational Workflow:
The GAN model generates additional high-quality EL images from a limited number of samples, which are then used to train the CNN model for defect classification.
5：Data Analysis Methods:
The performance of the proposed solution is evaluated through accuracy and loss metrics during the training process and compared with existing machine learning models.
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