Convolutional Neural Network based Efficient Detector for Multicrystalline Photovoltaic Cells Defect Detection

References

• Aghaei, M., A. Fairbrother, A. Gok, S. Ahmad, S. Kazim, K. Lobato, G. Oreski, A. Reinders, J. Schmitz, M. Theelen, et al. 2022. Review of degradation and failure phenomena in photovoltaic modules. Renewable and Sustainable Energy Reviews 159:112160. doi:10.1016/j.rser.2022.112160.
   Web of Science ®Google Scholar
• Al‐Waisy, A. S., D. A. Ibrahim, D. A. Zebari, Hammadi, S., Mohammed, H., Mohammed, M. A., and Damaševičius, R. 2022. Identifying defective solar cells in electroluminescence images using deep feature representations. Peer Journal Computer Science 8:e992. doi:10.7717/peerj-cs.992.
   PubMed Web of Science ®Google Scholar
• Anwar, S. A., and M. Z. Abdullah. 2014. Micro-crack detection of multicrystalline solar cells featuring an improved anisotropic diffusion filter and image segmentation technique. EURASIP Journal on Image and Video Processing 2014 (1):1e17. doi:10.1186/1687-5281-2014-15.
   Google Scholar
• Ara´ujo, R. L., F. H. D. Ara´ujo, and R. R. E. Silva. 2022. Automatic segmentation of melanoma skin cancer using transfer learning and fine-tuning. Multimedia Systems 28 (4):1239–50. doi:10.1007/s00530-021-00840-3.
   Web of Science ®Google Scholar
• Azizpour, H., A. Sharif Razavian, J. Sullivan, A. Maki, S. Carlsson. 2015. From generic to specific deep representations for visual recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops, Boston, MA, USA, 36–45.
   Google Scholar
• Buerhop-Lutz, C., S. Deitsch, and A. Maier . 2018. A benchmark for visual identification of defective solar cells in electroluminescence imagery[C]//35th European PV Solar Energy Conference and Exhibition, Brussels 12871289: 1287–89.
   Google Scholar
• Chen, A., X. Li, H. Jing, Hong, C., and Li, M. 2023. Anomaly detection algorithm for photovoltaic cells based on lightweight multi-channel spatial attention mechanism. Energies 16 (4):1619. doi:10.3390/en16041619.
   Web of Science ®Google Scholar
• Deitsch, S., V. Christlein, S. Berger, Buerhop-Lutz, C., Maier, A., Gallwitz, F., and Riess, C. 2019. Automatic classification of defective photovoltaic module cells in electroluminescence images. Solar Energy 185:455–68. doi:10.1016/j.solener.2019.02.067.
   Web of Science ®Google Scholar
• De Rose, R., A. Malomo, P. Magnone, Crupi, F., Cellere, G., Martire, M., Tonini, D., and Sangiorgi, E. 2012. A methodology to account for the finger interruptions in solar cell performance. Microelectronics Reliability 52 (9–10):2500–03. doi:10.1016/j.microrel.2012.07.014.
   Web of Science ®Google Scholar
• Everingham, M., S. A. Eslami, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman. 2015. The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision 111 (1):98–136. doi:10.1007/s11263-014-0733-5.
   Web of Science ®Google Scholar
• Haiyong, C., Z. Peng, and Y. Haowei. 2021. Crack detection based on multi-scale faster rcnn with attention. Opto-Electronic Engineering 48 (1):200112–1. doi:10.12086/oee.2021.200112.
   Google Scholar
• He, Y., K. Song, Q. Meng, and Y. Yan. 2019. An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Transactions on Instrumentation and Measurement 69 (4):1493–504. doi:10.1109/TIM.2019.2915404.
   Web of Science ®Google Scholar
• IRENA. 2022. World Energy Transitions Outlook 2022: 1.5° C Pathway[J]. Abu Dhabi: International Renewable Energy Agency. https://www.irena.org/publications/2022/Mar/World-Energy-Transitions-Outlook-2022
   Google Scholar
• Jia, C., Y. Yang, and, and Y. Xia. 2021. Scaling up visual and vision-language representation learning with noisy text supervision[C]//international conference on machine Learning. PMLR, Vienna, Austria 2021: 4904–16.
   Google Scholar
• Jocher, G., A. Chaurasia, and A. Stoken. 2022. Ultralytics/Yolov5: V7.0-yolov5 sota realtime instance segmentation 6.
   Google Scholar
• Köntges, M., I. Kunze, S. Kajari-Schröder, Breitenmoser, X., and Bjørneklett, B. 2011. The risk of power loss in crystalline silicon based photovoltaic modules due to micro-cracks. Solar Energy Materials and Solar Cells 95 (4):1131–37. doi:10.1016/j.solmat.2010.10.034.
   Web of Science ®Google Scholar
• Köntges, M., S. Kurtz, and C. E. Packard . 2014. Review of failures of photovoltaic modules. Task 13, IEA-PVPS. https://repository.supsi.ch/9645/
   Google Scholar
• Kumar, A., A. Raghunathan, R. Jones, T. Ma, and P. Liang. 2022. Fine-tuning can distort pretrained features and underperform out-of-distribution. International Conference on Learning Representations.
   Google Scholar
• Lee, Y., J.-W. Hwang, S. Lee, Y. Bae, and J. Park. 2019. An energy and gpu-computation efficient backbone network for real-time object detection. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Long Beach, CA, USA, 0–0.
   Google Scholar
• Li, C., L. Li, H. Jiang, K. Weng, Y. Geng, L. Li, Z. Ke, Q. Li, M. Cheng, W. Nie, et al. 2022. Yolov6: A single-stage object detection framework for industrial applications. arXiv preprint arXiv:2209.02976.
   Google Scholar
• Liu, Z., Y. Lin, Y. Cao, H. Hu, Y. Wei, Z. Zhang, S. Lin, and B. Guo. 2021. Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, QC, Canada, 10012–22.
   Google Scholar
• Liu, Z., H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie. 2022. A convnet for the 2020s. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 11976–86.
   Google Scholar
• Meng, Z., S. Xu, L. Wang, Y. Gong, X. Zhang, and Y. Zhao. 2022. Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning. Energy Science and Engineering 10 (3):800–13. doi:10.1002/ese3.1056.
   Web of Science ®Google Scholar
• Radford, A., J. W. Kim, and C. Hallacy. 2021. Learning transferable visual models from natural language supervision[C]//International conference on machine learning. PMLR, Vienna, Austria 2021: 8748–63.
   Google Scholar
• Ruiz, N., Y. Li, V. Jampani, Y. Pritch, M. Rubinstein, and K. Aberman. 2023. Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation, Vancouver, British Columbia, 22500–22510.
   Google Scholar
• Shou, C., L. Hong, W. Ding, et al. Defect detection with generative adversarial networks for electroluminescence images of solar cells[C]//2020 35th Youth Academic Annual Conference of Chinese Association of Automation (YAC). IEEE, 2020: 312–17. doi:10.1109/YAC51587.2020.9337676.
   Google Scholar
• Sridharan, N. V., and V. Sugumaran. 2021. Convolutional neural network based automatic detection of visible faults in a photovoltaic module. Energy Sources, Part A: Recovery, Utilization, & Environmental Effects 2021:1–16. doi:10.1080/15567036.2021.1905753.
   Google Scholar
• Stromer, D., A. Vetter, H. C. Oezkan, C. Probst, and A. Maier. 2019. Enhanced crack segmentation (eCS): A reference algorithm for segmenting cracks in multicrystalline silicon solar cells. IEEE J Photovoltaics 9 (3):752–58. doi:10.1109/JPHOTOV.2019.2895808.
   Web of Science ®Google Scholar
• Su, B., H. Chen, P. Chen, G. Bian, K. Liu, and W. Liu. 2020. Deep learning-based solar-cell manufacturing defect detection with complementary attention network. IEEE Transactions on Industrial Informatics 17 (6):4084–95. doi:10.1109/TII.2020.3008021.
   Web of Science ®Google Scholar
• Su, B., H. Chen, and Z. Zhou. 2021. Baf-detector: An efficient cnn-based detector for photovoltaic cell defect detection. IEEE Transactions on Industrial Electronics 69 (3):3161–71. doi:10.1109/TIE.2021.3070507.
   Web of Science ®Google Scholar
• Su, B., Z. Zhou, and H. Chen. 2022. Pvel-ad: A large-scale open-world dataset for photovoltaic cell anomaly detection. IEEE Transactions on Indus- Trial Informatics 19 (1):404–13. doi:10.1109/TII.2022.3162846.
   Web of Science ®Google Scholar
• Swati, Z. N. K., Q. Zhao, M. Kabir, F. Ali, Z. Ali, S. Ahmed, and J. Lu. 2019. Brain tumor classification for mr images using transfer learning and fine- tuning. Computerized Medical Imaging and Graphics 75:34–46. doi:10.1016/j.compmedimag.2019.05.001.
   PubMed Web of Science ®Google Scholar
• Tajbakhsh, N., J. Y. Shin, S. R. Gurudu, R. T. Hurst, C. B. Kendall, M. B. Gotway, and J. Liang. 2016. Convolutional neural networks for medical image analysis: Full training or fine tuning? IEEE Transactions on Medi- Cal Imaging 35 (5):1299–312. doi:10.1109/TMI.2016.2535302.
   PubMed Web of Science ®Google Scholar
• Tang, W., Q. Yang, X. Hu, and W. Yan. 2022. Convolution neural network based polycrystalline silicon photovoltaic cell linear defect diagnosis using elec- troluminescence images. Expert Systems with Applications 202:117087. doi:10.1016/j.eswa.2022.117087.
   Web of Science ®Google Scholar
• Tang, W., Q., Yang, and W., Yan. 2019. Deep learning based model for defect detection of mono-crystalline-si solar pv module cells in electroluminescence images using data augmentation[C]//2019. IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), 1–5. IEEE. doi:10.1109/APPEEC45492.2019.8994713.
   Google Scholar
• Tsai, D. M., S. C. Wu, and W. Y. Chiu. 2013. Defect detection in solar modules using ICA basis images. IEEE Transactions on Industrial Informatics 9 (1):122–31. doi:10.1109/TII.2012.2209663.
   Web of Science ®Google Scholar
• Tsai, D. M., S. C. Wu, and W. C. Li. 2012. Defect detection of solar cells in electroluminescence images using Fourier image reconstruction. Solar Energy Materials and Solar Cells 99:250e62. doi:10.1016/j.solmat.2011.12.007.
   Web of Science ®Google Scholar
• ValizadehAslani, T., Y. Shi, J. Wang, P. Ren, Y. Zhang, M. Hu, L. Zhao, and H. Liang. 2022. Two-stage fine-tuning: A novel strategy for learning class- imbalanced data. arXiv preprint arXiv: 2207.10858.
   Google Scholar
• Vaswani, A., N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. –. Kaiser, and I. Polosukhin. 2017. Attention is all you need, Advances in neural information processing systems, Long Beach, CA, USA, 30.
   Google Scholar
• Wang, J., L. Bi, P. Sun, Jiao, P., Ma, X., Lei, X., and Luo, Y. 2022. deep-learning-based automatic detection of photovoltaic cell defects in electroluminescence images. Sensors. 23 (1):297. doi:10.3390/s23010297.
   PubMed Web of Science ®Google Scholar
• Wang, C. Y., A., Bochkovskiy, and H. Y. M., Liao. 2023. Yolov7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, Vancouver, British Columbia, 7464–75.
   Google Scholar
• Wang, C.-Y., H.-Y. M., Liao, Y.-H., Wu, P.-Y., Chen, J.-W., Hsieh, and I.-H., Yeh. 2020. Cspnet: A new backbone that can enhance learning capability of cnn. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, Seattle, WA, USA, 390–91.
   Google Scholar
• Wang, C.-Y., H.-Y. M. Liao, and I.-H. Yeh. 2022. Designing network design strategies through gradient path analysis. arXiv preprint arXiv: 2211.04800.
   Google Scholar
• Wang, Y., L. Li, Y. Sun, J. Xu, Y. Jia, J. Hong, X. Hu, G. Weng, X. Luo, S. Chen, et al. 2021. Adaptive automatic solar cell defect detection and classification based on absolute electroluminescence imaging. Energy 229:120606. doi:10.1016/j.energy.2021.120606.
   Web of Science ®Google Scholar
• Wortsman, M., G. Ilharco, J. W. Kim, M. Li, S. Kornblith, R. Roelofs, R. G. Lopes, H. Hajishirzi, A. Farhadi, H. Namkoong, et al. 2022. Robust fine-tuning of zero-shot models. In: Proceedings of the IEEE/CVF Con- ference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 7959–71.
   Google Scholar
• Zhang, M., and L. Yin. 2022. Solar cell surface defect detection based on improved YOLO v5. IEEE Access 10:80804–15. doi:10.1109/ACCESS.2022.3195901.
   Google Scholar
• Zhao, Y., K. Zhan, Z. Wang, and W. Shen. 2021. Deep learning-based automatic detection of multitype defects in photovoltaic modules and application in real production line. Progress in Photovoltaics: Research and Applications 29 (4):471–84. doi:10.1002/pip.3395.
   Web of Science ®Google Scholar
• Zhu, X., S. Lyu, X. Wang, and Q. Zhao. 2021. Tph-yolov5: Improved yolov5 based on transformer prediction head for object detection on drone-captured scenarios. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, Montreal, BC, Canada, 2778–88.
   Google Scholar