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Solar module defects classification using deep convolutional neural network

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The classification model achieved a high accuracy of 97.85% for detecting solar module defects.
Using a dataset of 8,370 images, the model categorized defects into five types: clean, dirt, burn, crack, and snail track.
The combination of Deep Convolutional Neural Networks and DenseNet201 provided the best classification performance.
This method can streamline maintenance in solar power plants by quickly identifying and categorizing defects.

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This system employs deep learning to classify solar module defects, highlighting the role of data augmentation and transfer learning.

Cahyaningtyas et al. (2025) studied this question.

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