Abstract
Nowadays, the photovoltaic industry has developed significantly. Solar photovoltaic panel defect detection is an important part of solar photovoltaic panel quality inspection. Aiming at the problems of chaotic distribution of defect targets on photovoltaic panels, large scale span and blurred features, this paper improves the network structure based on the YOLOv5 model, which can better cope with the defect detection under various conditions. This paper mainly optimizes the following three aspects. Firstly, for the defect targets that are not of the right scale, the SE attention module and GhostBottleneck are introduced on the basis of the YOLOv5 model, which accelerates the extraction of useless features and enhances the precision of small object perception by capturing the features of defects of different scales and fusing semantic features of different depths. Secondly, Ghostconv is introduced to increase the receptive scope of the feature map, which makes the extracted feature discrimination ability stronger, and effectively enhances the defect detection ability of the model. Finally, by utilizing the ELU activation function instead of the Leaky ReLU activation function, the problem of gradient explosion and gradient disappearance is solved, and the model convergence speed is accelerated. Experimental results demonstrate that the improved YOLOv5 model can effectively detect the defects of photovoltaic panels, and the mAP reaches 92.4%, which is 16.2% higher than the original algorithm.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Romero-Cadaval, E., et al.: Grid-connected photovoltaic generation plants: components and operation. Ind. Electr. Mag. IEEE 7(3), 6–20 (2013)
Bin, S.: Research progress and development prospect of solar photovoltaic power generation materials. China Powder Ind. (1), 22–24 (2020)
Wang, Y., Sun, Z., Zhao, B.: Zhao does not bribe. Cracking detection of silicon wafers of solar cells based on machine vision. Comb. Mach. Tool Autom. Process. Technol. (12), 95–97 (2019)
Balzateguo, J., Eciolaza, L., Arexolaleiba, A.: Defect detection on polycrystalline solar cells using electroluminescence and fully convolutional nerural networks. In 2020IEEE/SICE International Symposium on System Integration(SH), pp. 949–953 (2020)
Chen, H.Y., et al.: Accurate and robust crack detection using steerable evidence filtering in electro-luminescence images of solar cells. Opt. Lasers Eng. 118, 22–33 (2019)
Xiaoliang, Q., et al.: Surface defect detection of solar cells based on machine vision saliency. J. Instrum. 38(7), 1570–1578 (2017)
Ying, Z., et al.: Application of improved CNN in defect detection of solar panels. Comput. Simul. 37(3), 458–463 (2020)
Yunyan, W., Zhigang, Z., Shuai, L.: Data enhanced defect detection of solar cells. J. Electr. Measur. Instrum. 35(1), 26–32 (2021)
Hinton, G.E., Salakhutdinov, R.: Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Chen, H., et al.: Solar cell surface defect inspection based on multispectral convolutional neural network. J. Intell. Manuf. 31, 453–468 (2020)
Shanableh, T.: Saliency detection in MPEG and HEVC video using intra-frame and inter-frame distances. SIViP 10, 703–709 (2016)
Akram, M.W., et al.: CNN based automatic detection of photovaoltaic cell defects in electroluminescence images. Engergy 189, 116319 (2019)
Shuqing, W., et al.: Surface defect detection of solar cells based on improved YOLOv5s. Instrum. Technol. Sens. 5, 111–116 (2022)
Han, K., et al.: GhostNet: more feature feom cheap operations. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Patten Recognition, pp. 1580–1589 (2020)
Zhou, T., Jing, X.: Surface-based detection and 6-Dof pose estimation of 3-D objects in cluttered scenes. IEEE Trans. Robot. (99), 1–15 (2016)
Janssen, P., Swanepeol, J.: Efficiency behaviour of kernel-smoothed kernel distribution function estimators. South Afr. Stat. J. 54(1), 15–23 (2020)
Yang, L., et al.: Research on fault diagnosis method of photovoltaic module. Mech. Des. Manuf. (12), 82–87 (2021)
Liu, W., et al.: SSD: single shot MultiBox de-tector[EB/OL] (2015). arXiv:1512.02325. https://arxiv.org/abs/1512.02325
Redmon, J., et al.: You only look once:unified,real-time object detection. In: 2016 IEEE conference on Computer Vision and Pattern Recognition, June 27–30, 2016, Las Vegas, NV, USA, pp. 779–788. IEEE Press, New York (2016)
Lin, T.Y., et al.: Focal loss for dense object detection. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 318–327 (2020)
Zhao, Q.J., et al.: M2Det: a single-shot object detector based on multi-level feature pyramid network. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, pp. 9259–9266 (2019)
Li, L., Wang, Z., Zhang, T.: GBH-YOLOv5: ghost convolution with BottleneckCSP and tiny target prediction head incorporating YOLOv5 for PV panel defect detection. Electronic 12, 561 (2023)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
About this paper
Cite this paper
Teng, S., Liu, Z., Luo, Y., Zhang, P. (2024). Improved Solar Photovoltaic Panel Defect Detection Technology Based on YOLOv5. In: Li, J., Zhang, B., Ying, Y. (eds) 6GN for Future Wireless Networks. 6GN 2023. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 553. Springer, Cham. https://doi.org/10.1007/978-3-031-53401-0_19
Download citation
DOI: https://doi.org/10.1007/978-3-031-53401-0_19
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-53400-3
Online ISBN: 978-3-031-53401-0
eBook Packages: Computer ScienceComputer Science (R0)