- 标题
- 摘要
- 关键词
- 实验方案
- 产品
-
Deep learning based automatic defect identification of photovoltaic module using electroluminescence images
摘要： The maintenance of large-scale photovoltaic (PV) power plants is considered as an outstanding challenge for years. This paper presented a deep learning-based defect detection of PV modules using electroluminescence images through addressing two technical challenges: (1) providing a large number of high-quality Electroluminescence (EL) image generation method for the limit of EL image samples; and (2) an efficient model for automatic defect classification with the generated EL image. The EL image generation approach combines traditional image processing technology and GAN characteristics. It can produce a large number of EL image samples with high resolution using a limited number of samples. Then, a convolution neural network (CNN) based model for the automatic classification of defects in an EL image is presented. CNN is used to extract the deep feature of the EL image. It can greatly increase the accuracy and efficiency of PV modules inspection and health management in comparison with the other solutions. The proposed solution is assessed through extensive experiments by using the existing machine learning models, VGG16, ResNet50, Inception V3 and MobileNet, as the comparison benchmarks. The numerical results confirm that the proposed deep learning-based solution can carry out efficient and accurate defect detection automatically using the electroluminescence images.
关键词： Automatic defect classification,Electroluminescence Images,Generative adversarial network,Convolution neural network
更新于2025-09-23 15:19:57
-
[IEEE 2019 IEEE International Conference on Big Knowledge (ICBK) - Beijing, China (2019.11.10-2019.11.11)] 2019 IEEE International Conference on Big Knowledge (ICBK) - U-Net Based Defects Inspection in Photovoltaic Electroluminecscence Images
摘要： Efficient defects segmentation from photovoltaic (PV) electroluminescence (EL) images is a crucial process due to the random inhomogeneous background and unbalanced crack non-crack pixel distribution. The automatic defect inspection of solar cells greatly influences the quality of photovoltaic cells, so it is necessary to examine defects efficiently and accurately. In this paper we propose a novel end to end deep learning-based architecture for defects segmentation. In the proposed architecture we introduce a novel global attention to extract rich context information. Further, we modified the U-net by adding dilated convolution at both encoder and decoder side with skip connections from early layers to later layers at encoder side. Then the proposed global attention is incorporated into the modified U-net. The model is trained and tested on Photovoltaic electroluminescence 512x512 images dataset and the results are recorded using mean Intersection over union (IOU). In experiments, we reported the results and made comparison between the proposed model and other state of the art methods. The mean IOU of proposed method is 0.6477 with pixel accuracy 0.9738 which is better than the state-of-the-art methods. We demonstrate that the proposed method can give effective results with smaller dataset and is computationally efficient.
关键词： cracks detection,electroluminescence images,U-net,Solar cell defects detection
更新于2025-09-16 10:30:52