Automatic Crack Segmentation and Feature Extraction in Electroluminescence Images of Solar Modules

Chen, Xin; Karin, Todd; Libby, Cara; Deceglie, Michael G.; Hacke, Peter; Silverman, Tim; Jain, Anubhav

doi:10.1109/jphotov.2023.3249970

Cited by 7 publications

(4 citation statements)

References 38 publications

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“…The crack damage estimation described in Section VI-A achieved a Dice coefficient of 0.601. While this result did not exceed the findings in [26], it is noteworthy to emphasize that both crack and cold soldering segmentation possess the advantage of not requiring manual creation of labeled images for each cell image in the dataset.…”

Section: ) Crack Segmentationcontrasting

confidence: 49%

“…More interestingly, the authors demonstrate how ViT networks outperform the more traditional ML approaches in terms of accuracy. As for studies regarding damage quantification, [26] proposes using a UNet model to perform a semantic segmentation of the image highlighting any pixel belonging to an affected area.…”

Section: Related Workmentioning

confidence: 99%

“…Although authors in [25] implemented the ViT approach for PP flaw detection, no flaw type classification was carried out; and while [26] evaluates the affected area caused by a crack, it requires label images to be drawn manually for the training of the model. Additionally, no damage quantification of cold solders on solar cells has been carried out yet either.…”

Section: Related Workmentioning

confidence: 99%

See 2 more Smart Citations

Quantification of Damages and Classification of Flaws in Mono-Crystalline Photovoltaic Cells Through the Application of Vision Transformers

Barnabé,

Jiménez,

Fraidenraich

et al. 2023

IEEE Access

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Section: ) Crack Segmentationcontrasting

confidence: 49%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Quantification of Damages and Classification of Flaws in Mono-Crystalline Photovoltaic Cells Through the Application of Vision Transformers

Barnabé,

Jiménez,

Fraidenraich

et al. 2023

IEEE Access

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show abstract

“…However, with the rising use of photovoltaic and ongoing installation of large-scale photovoltaic systems worldwide, the manual inspection methods and volt-ampere characteristics of the integrated tester methods do not meet the demand. They have lower detection efficiency and fewer types of defects 2 . Therefore, it is crucial to promptly and accurately detect defects in photovoltaic cells to ensure long-term stable operation of the PV power generation system.…”

Section: Introductionmentioning

confidence: 99%

Defect detection of photovoltaic modules based on improved VarifocalNet

Jia,

Chen,

Zhao

2024

Sci Rep

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Detecting and replacing defective photovoltaic modules is essential as they directly impact power generation efficiency. Many current deep learning-based methods for detecting defects in photovoltaic modules focus solely on either detection speed or accuracy, which limits their practical application. To address this issue, an improved VarifocalNet has been proposed to enhance both the detection speed and accuracy of defective photovoltaic modules. Firstly, a new bottleneck module is designed to replace the first bottleneck module of the last stage convolution group in the backbone. This new module includes both standard convolution and dilated convolution, enabling an increase in network depth and receptive field without reducing the output feature map size. This improvement can help to enhance the accuracy of defect detection for photovoltaic modules. Secondly, another bottleneck module is also designed and used to replace the original bottleneck module used in the fourth stage convolution group of the backbone. This new module has smaller parameters than the original bottleneck module, which is useful to improve the defect detection speed of the photovoltaic module. Thirdly, a feature interactor is designed in the detection head to enhance feature expression in the classification branch. This helps improve detection accuracy. Besides, an improved intersection over union is proposed and introduced into the loss function to measure the difference between the predicted and ground truth boxes. This is useful for improving defect detection accuracy. Compared to other methods, the proposed method has the highest detection accuracy. Additionally, it also has a faster detection speed than other methods except for the DDH-YOLOv5 method and the improved YOLOv7 method.

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SIIF: Semantic information interactive fusion network for photovoltaic defect segmentation

Zhou,

Wang,

Wang

et al. 2024

Applied Energy

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Automatic Crack Segmentation and Feature Extraction in Electroluminescence Images of Solar Modules

Cited by 7 publications

References 38 publications

Quantification of Damages and Classification of Flaws in Mono-Crystalline Photovoltaic Cells Through the Application of Vision Transformers

Quantification of Damages and Classification of Flaws in Mono-Crystalline Photovoltaic Cells Through the Application of Vision Transformers

Defect detection of photovoltaic modules based on improved VarifocalNet

SIIF: Semantic information interactive fusion network for photovoltaic defect segmentation

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