article · IEEE Access
This research introduces a novel method employing various deep learning techniques for automated defect identification in solar cell images. The study comprehensively evaluated 24 distinct convolutional neural network (CNN) architectures, including high-performance and lightweight models suitable for resource-constrained settings. Using a balanced dataset of 3,102 solar cell images with common faults, MobileNetV2 and Xception demonstrated exceptional performance, achieving accuracy rates of 99.95% and 99.29% respectively, with minimal validation losses. The findings highlight the potential of efficient models like MobileNetV2 for real-world applications in solar energy generation, suggesting they could significantly enhance quality control systems by providing a reliable and efficient defect detection method.
Ensuring the quality of solar cells is crucial for efficient and reliable solar energy production. This research offers an automated, highly accurate way to detect defects, which can reduce manufacturing costs and improve the lifespan and performance of solar panels, making solar power more accessible and dependable.
This research provides a foundation for developing automated quality control systems for solar cell manufacturing. Solar cell producers and quality assurance departments could utilise these deep learning models to efficiently identify defects. The abstract indicates this is applied research demonstrating potential for real-world use, suggesting it is nearing market readiness for integration into industrial production lines.
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This research study introduces a unique method that makes use of a wide range of deep learning (DL) techniques for automated flaw identification in solar cell images. The research paper investigates how well 24 distinct convolutional neural network (CNN) architectures— Residual network (ResNet), densely connected convolutional networks (DenseNet), visual geometry group (VGG), Inception, mobile network (MobileNet), Xception, SqueezeNet, and AlexNet—classify solar cells into defected and non-defective categories. This study is interesting since it does a thorough assessment of a wide variety of models and concentrates on high-performance architectures and lightweight models that may be used in contexts with limited resources. The research paper performed our studies using a balanced and well-curated dataset of 3,102 images of solar cells with a range of common faults. MobileNetV2 and Xception demonstrated excellent performance in defect identification, with accuracy rates of 99.95% and 99.29% respectively, with minimal validation losses. This study demonstrates the potential of efficient models such as MobileNetV2 for real-world use in solar energy generation. It also provides a detailed comparison of several DL models. The results suggest that the inclusion of these models might significantly enhance quality control systems, offering a reliable and efficient method for detecting flaws in solar cells.
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DOI: 10.1109/access.2024.3525183
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