Optimizing feature extraction and fusion for high-resolution defect detection in solar cells
In this paper, we propose a novel architecture for defect detection in electroluminescent images of polycrystalline silicon solar cells, addressing the challenges posed by subtle and dispersed defects. Our model, based on a modified Swin Transformer, incorporates key innovations that enhance feature...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Intelligent Systems with Applications |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2667305324001170 |
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| _version_ | 1850244707380101120 |
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| author | Hoanh Nguyen Tuan Anh Nguyen Nguyen Duc Toan |
| author_facet | Hoanh Nguyen Tuan Anh Nguyen Nguyen Duc Toan |
| author_sort | Hoanh Nguyen |
| collection | DOAJ |
| description | In this paper, we propose a novel architecture for defect detection in electroluminescent images of polycrystalline silicon solar cells, addressing the challenges posed by subtle and dispersed defects. Our model, based on a modified Swin Transformer, incorporates key innovations that enhance feature extraction and fusion. We replace the conventional self-attention mechanism with a novel group self-attention mechanism, increasing the mAP50:5:95 score from 50.12 % to 52.98 % while reducing inference time from 74 ms to 62 ms. We also introduce a spatial displacement with shift convolution module, replacing the traditional Multi-Layer Perceptron, which further enhances the model's receptive field and improves precision and recall. Additionally, our fast multi-scale feature fusion mechanism effectively combines high-resolution details with high-level semantic features from different network layers, optimizing defect detection accuracy. Experimental results on the PVEL-AD dataset demonstrate that our model achieves the highest mAP50 score of 83.11 % and an F1-Score of 84.33 %, surpassing state-of-the-art models while maintaining a competitive inference time of 66.3 ms. These findings highlight the effectiveness of our innovations in improving defect detection accuracy and computational efficiency, making our model a robust solution for quality assurance in solar cell manufacturing. |
| format | Article |
| id | doaj-art-e621ed40a9744e328d85bbdbbe509fba |
| institution | OA Journals |
| issn | 2667-3053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Intelligent Systems with Applications |
| spelling | doaj-art-e621ed40a9744e328d85bbdbbe509fba2025-08-20T01:59:39ZengElsevierIntelligent Systems with Applications2667-30532024-12-012420044310.1016/j.iswa.2024.200443Optimizing feature extraction and fusion for high-resolution defect detection in solar cellsHoanh Nguyen0Tuan Anh Nguyen1Nguyen Duc Toan2Corresponding author.; Faculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, VietnamFaculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, VietnamFaculty of Electrical Engineering Technology, Industrial University of Ho Chi Minh City, Ho Chi Minh City 700000, VietnamIn this paper, we propose a novel architecture for defect detection in electroluminescent images of polycrystalline silicon solar cells, addressing the challenges posed by subtle and dispersed defects. Our model, based on a modified Swin Transformer, incorporates key innovations that enhance feature extraction and fusion. We replace the conventional self-attention mechanism with a novel group self-attention mechanism, increasing the mAP50:5:95 score from 50.12 % to 52.98 % while reducing inference time from 74 ms to 62 ms. We also introduce a spatial displacement with shift convolution module, replacing the traditional Multi-Layer Perceptron, which further enhances the model's receptive field and improves precision and recall. Additionally, our fast multi-scale feature fusion mechanism effectively combines high-resolution details with high-level semantic features from different network layers, optimizing defect detection accuracy. Experimental results on the PVEL-AD dataset demonstrate that our model achieves the highest mAP50 score of 83.11 % and an F1-Score of 84.33 %, surpassing state-of-the-art models while maintaining a competitive inference time of 66.3 ms. These findings highlight the effectiveness of our innovations in improving defect detection accuracy and computational efficiency, making our model a robust solution for quality assurance in solar cell manufacturing.http://www.sciencedirect.com/science/article/pii/S2667305324001170Feature extractionFeature fusionSwin transformerDefect detectionElectroluminescent images |
| spellingShingle | Hoanh Nguyen Tuan Anh Nguyen Nguyen Duc Toan Optimizing feature extraction and fusion for high-resolution defect detection in solar cells Intelligent Systems with Applications Feature extraction Feature fusion Swin transformer Defect detection Electroluminescent images |
| title | Optimizing feature extraction and fusion for high-resolution defect detection in solar cells |
| title_full | Optimizing feature extraction and fusion for high-resolution defect detection in solar cells |
| title_fullStr | Optimizing feature extraction and fusion for high-resolution defect detection in solar cells |
| title_full_unstemmed | Optimizing feature extraction and fusion for high-resolution defect detection in solar cells |
| title_short | Optimizing feature extraction and fusion for high-resolution defect detection in solar cells |
| title_sort | optimizing feature extraction and fusion for high resolution defect detection in solar cells |
| topic | Feature extraction Feature fusion Swin transformer Defect detection Electroluminescent images |
| url | http://www.sciencedirect.com/science/article/pii/S2667305324001170 |
| work_keys_str_mv | AT hoanhnguyen optimizingfeatureextractionandfusionforhighresolutiondefectdetectioninsolarcells AT tuananhnguyen optimizingfeatureextractionandfusionforhighresolutiondefectdetectioninsolarcells AT nguyenductoan optimizingfeatureextractionandfusionforhighresolutiondefectdetectioninsolarcells |