Defect Detection Algorithm for Monocrystalline Silicon Solar Cell Modules Based on Image Processing and Deep Learning
Bibliographic record
Abstract
No abstract is available for this record. The assistant can identify its topic from the title, but cannot infer the study’s results.
Source: Not provided · Quality: missing
Research context
OpenAlex topics: Industrial Vision Systems and Defect Detection · Silicon and Solar Cell Technologies
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (32)
Show source-provided references
1. Ballif, C., Haug, F.J., Boccard, M., et al.: Status and perspectives of crystalline silicon photovoltaics in research and industry[J]. Nat. Reviews Mater. 7(8), 597–616 (2022)10.1038/s41578-022-00423-2 ↗
2. Fuyuki, T., Kondo, H., Yamazaki, T., et al.: Photographic surveying of minority carrier diffusion length in polycrystalline silicon solar cells by electroluminescence[J]. Appl. Phys. Lett., 86(26). (2005)10.1063/1.1978979 ↗
3. Zhang, W., Ma, Y., Bai, X., et al.: Defect identification of solar panels using improved faster R-CNN[J]. Power Syst. Technol. 46(7), 2593–2600 (2022)
4. Chen, H.Y., Zhao, P., Yao, H.W.: Crack detection based on multi-scale faster RCNN with attention[J]. Opto-Electronic Eng. 48(1), 200112 (2021)
5. Chen, A., Li, X., Jing, H., et al.: Anomaly detection algorithm for photovoltaic cells based on lightweight multi-channel Spatial attention mechanism[J]. Energies. 16(4), 1619 (2023)10.3390/en16041619 ↗
6. Zhang, X., Hao, Y., Hong, S., et al.: Detection of surface defects on solar cells by fusing Multi-channel Convolution neural networks[J]. Infrared Phys. Technol. 108, 103334 (2020)10.1016/j.infrared.2020.103334 ↗
7. Zhang, M., Yin, L.: Solar cell surface defect detection based on improved YOLOv5[J]. IEEE Access. 10, 80804–80815 (2022)10.1109/ACCESS.2022.3195901 ↗
8. Chen, H., Song, M., Zhang, Z., et al.: Detection of surface defects in solar cells by Bidirectional-Path feature pyramid Group-Wise attention Detector[J]. IEEE Trans. Industr. Electron. 71, 1–9 (2022)
9. Meng, Z., Xu, S., Wang, L., et al.: Defect object detection algorithm for electroluminescence image defects of photovoltaic modules based on deep learning[J]. Energy Sci. Eng. 10(3), 800–813 (2022)10.1002/ese3.1056 ↗
10. Mazen, F.M.A., Seoud, R.A.A., Shaker, Y.O.: Deep learning for automatic defect detection in PV modules using electroluminescence Images[J]. IEEE Access. 11, 57783–57795 (2023)10.1109/ACCESS.2023.3284043 ↗
11. Fu, H., Cheng, G.: Convolutional neural network based efficient detector for multicrystalline photovoltaic cells defect Detection[J]. Energy Sources Part. A-Recovery Utilization Environ. Eff. 45(3), 8686–8702 (2023)
12. Tiwari, S., Sharma, K.S.: .YOLOv7-tiny-based lightweight and efficient algorithm for photovoltaic cell crack detection[J]. J. Supercomputing 2025,81(8):990–99010.1007/s11227-025-07493-3 ↗
13. Chen, C., Chen, Z., ,Li, H., et al.: Research on defect detection in lightweight photovoltaic cells using YOLOv8-FSD[J].Sensors,2025,25(3):843–84310.3390/s25030843 ↗
14. Chen, S., Lu, Y., ,Qin, G., et al.: CSPD-DETR: Real-time silicon crystalline photovoltaic cell surface defect detection transformer for Building photovoltaic systems[J]. J. Building Eng., 108112810–108112810. (2025)10.1016/j.jobe.2025.112810 ↗
15. Li, Y., Hou, Q., Zheng, Z., et al.: Large selective kernel network for remote sensing object detection[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 16794–16805. (2023)10.1109/ICCV51070.2023.01540 ↗
16. Wang, C.Y., Liao, H.Y.M., Wu, Y.H., et al.: CSPNet: A new backbone that can enhance learning capability of CNN[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops. 390–391. (2020)10.1109/CVPRW50498.2020.00203 ↗
17. Tan, M., Pang, R., Le, Q.V., Efficientdet: Scalable and efficient object detection[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 10781–10790. (2020)10.1109/CVPR42600.2020.01079 ↗
18. Wang, J., Chen, K., Xu, R., et al.: Carafe: Content-aware reassembly of features[C]//Proceedings of the IEEE/CVF international conference on computer vision. 3007–3016. (2019)10.1109/ICCV.2019.00310 ↗
19. Zheng, Z., Wang, P., Liu, W., et al.: Distance-IoU loss: Faster and better learning for bounding box regression[C]//Proceedings of the AAAI conference on artificial intelligence. 34(07), 12993–13000. (2020)10.1609/aaai.v34i07.6999 ↗
20. Rezatofighi, H., Tsoi, N., Gwak, J.Y., et al.: Generalized intersection over union: A metric and a loss for bounding box regression[C]//Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. 658–666. (2019)10.1109/CVPR.2019.00075 ↗
21. Gevorgyan, Z.: SIoU loss: More powerful learning for bounding box regression[J]. arXiv preprint arXiv:2205.12740, (2022)
22. Ma, S., Xu, Y.: Mpdiou: A loss for efficient and accurate bounding box regression[J]. (2023). arXiv preprint arXiv:2307.07662
23. ZHAO, Y., ZHAN, K., WANG, Z., et al.: Deep learning-based automatic detection of multitype defects in photo-voltaic modules and application in real production line [J]. Process in Photovoltaics, 2021, 28(4), 471–48410.1002/pip.3395 ↗
24. Cui, Z., Leng, J., Liu, Y., et al.: SKNet: Detecting rotated ships as keypoints in optical remote sensing images[J]. IEEE Trans. Geosci. Remote Sens. 59(10), 8826–8840 (2021)10.1109/TGRS.2021.3053311 ↗
25. Chen, K., Wang, J., Pang, J., et al.: MMDetection: Open Mmlab detection toolbox and benchmark[J]. (2019). arXiv preprint arXiv:1906.07155
26. Liu, W., Anguelov, D., Erhan, D., et al.: Ssd: Single shot multibox detector[C]//Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. Springer International Publishing, 21–37. (2016)10.1007/978-3-319-46448-0_2 ↗
27. Ross, T.Y., Dollár, G.: Focal loss for dense object detection[C]//proceedings of the IEEE conference on computer vision and pattern recognition. 2980–2988. (2017)
28. Ren, S., He, K., Girshick, R., et al.: Faster R-CNN: Towards real-time object detection with region proposal networks[J]. IEEE Trans. Pattern Anal. Mach. Intell. 39(6), 1137–1149 (2016)10.1109/TPAMI.2016.2577031 ↗
29. Cai, Z., Vasconcelos, N.: Cascade r-cnn: Delving into high quality object detection[C]//Proceedings of the IEEE conference on computer vision and pattern recognition. 6154–6162. (2018)10.1109/CVPR.2018.00644 ↗
30. Lyu, C., Zhang, W., Huang, H., et al.: Rtmdet: An empirical study of designing real-time object detectors[J]. (2022). arXiv preprint arXiv:2212.07784
31. Zhao, Y., Lv, W., Xu, S., et al.: Detrs beat yolos on real-time object detection[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 16965–16974. (2024)10.1109/CVPR52733.2024.01605 ↗
32. Feng, C., Zhong, Y., Gao, Y., et al.: Tood: Task-aligned one-stage object detection[C]//2021 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE Computer Society, 3490–3499. (2021)10.1109/ICCV48922.2021.00349 ↗