Abstract
With the global energy shortage, countries all over the world are vigorously develo** new energy sources, and photovoltaic glass, as an important raw material for photovoltaic power generation, puts forward higher requirements for its output and quality. In order to solve the problems of low efficiency, susceptibility to interference by human factors, and low detection accuracy during the detection of photovoltaic glass edge defects by traditional manual methods, this paper proposes an automatic detection method of photovoltaic glass edge defects based on machine vision technology. Firstly, a machine vision defect detection system is designed to meet the needs of photovoltaic glass edge defect detection, includes high-contrast imaging solutions with a combination of multiple light sources for illumination and an automated transmission scheme with high stability, and an image dataset is established; Secondly, according to the characteristics of defect detection process with many interfering factors and high requirements for detection efficiency and accuracy, a deep learning defect detection method is proposed to improve the SqueezeNet model, which incorporates dense residual units into the two-part Fire Module of the classical SqueezeNet network model to extract the important feature information of the glass edge image, and effectively avoids the influence of interfering factors, such as water droplets, on the detection of defects. Finally, the improved SqueezeNet network algorithm is applied in the machine vision glass edge defect detection system designed in this paper. The experimental results show that the average leakage rate of the photovoltaic glass edge defect detection method proposed in this paper is 0.0064%, the misdetection detection rate is 0.0075%, and the average detection time is 2.715 s, and can meet the requirements of the automated production of photovoltaic glass.
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References
Andrew, G., Zhu, M., Chen, B., Dmitry K., Wang, W., Tobias, W., Marco, A., Hartwig, A.: MobileNets: efficient convolutional neural networks for mobile vision applications. ar**v:1704.04861 (2017).
Bao, Z., Xue, R., Hu, J., Liu, Y.: Color image encryption based on lite dense-ResNet and bit-XOR diffusion. Multimed. Tool. Appl. 10, 1–12 (2023). https://doi.org/10.1007/10.1007/s11042-023-16073-7
Benjamin, L., Robin, O., Simon, R., Ian, B., Gavin, S., Peter, S., Christina, S., Lina, G., Anne, A., Jonathan, B., Paul, A., Stefan, K.: Towards improved cover glasses for photovoltaic devices. Prog. Photovoltaics. 28(11), 1187–1206 (2020). https://doi.org/10.1002/pip.3334
Choi, H.: CVCC model: learning-based computer vision color constancy with RiR-DSN architecture. Sensors. 23(11), 5341 (2023). https://doi.org/10.3390/s23115341
Chang, M., Chen, B., Gabayno, J., Chen, M.: Development of an optical inspection platform for surface defect detection in touch panel glass. INT. J. Optomechatroni. 10(2), 63–72 (2016). https://doi.org/10.1080/15599612.2016.1166304
Chao, S., Tsai, D.: An anisotropic diffusion-based defect detection for low-contrast glass substrates. Image. Vision. Comput. 26(2), 187–200 (2008). https://doi.org/10.1016/j.imavis.2007.03.003
Dong, S., Chen, C., Liang, Y., Zou, K., Liu, G.: Defect detection of photovoltaic glass based on level set map. Neural Comput. Appl. 34(13), 10691–10705 (2022). https://doi.org/10.1007/s00521-022-07005-x
Fu, M., Wu, K., Li, Y., Luo, L., Huang, W., Zhang, Q.: An intelligent detection method for plasmodium based on self-supervised learning and attention mechanism. Life Sci. 10, 1117192 (2023). https://doi.org/10.3389/fmed.2023.1117192
Fu, G., Le, W., Zhang, Z., Li, J., Zhu, Q., Niu, F., Chen, H., Sun, F., Shen, Y.: A surface defect inspection model via rich feature extraction and residual-based progressive integration CNN. Machines. 11(1), 124 (2023). https://doi.org/10.3390/machines11010124
Fu, G., Zhang, Z., Le, W., Li, J., Zhu, Q., Niu, F., Chen, H., Sun, F., Shen, Y.: A multi-scale pooling convolutional neural network for accurate steel surface defects classification. Front. Neurorobotics. 17, 1096083 (2023). https://doi.org/10.3389/fnbot.2023.1096083
Forrest, N., Song, H., Matthew,W., Khalid, A., William, W., Kurt, K.: SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size. ar**v:1602.07360 (2016).
François, C.: Xception: deep learning with depthwise separable convolutions. ar**v:1610.02357 (2016).
Hui, Z., Lu, A.: A deep learning method combined with an electronic nose for gas information id-entification of soybean from different origins. Chemometrics Intell. Lab Syst. 240, 104906 (2023). https://doi.org/10.1016/j.chemolab.2023.104906
Hua, S., Li, B., Shu, L., Jiang, P., Cheng, S.J.M.: Defect detection method using laser vision with model-based segmentation for laser brazing welds on car body surface. Measurement 178, 109370 (2021). https://doi.org/10.1016/j.measurement.2021.109370
He, Y., Song, K., Meng, Q., Yan, Y.: An end-to-end steel surface defect detection approach via fusing multiple hierarchical features. IEEE Trans. Instrum. Meas. 69(4), 1493–1504 (2020). https://doi.org/10.1109/TIM.2019.2915404
Hu, J., Shen, L., Samuel, A., Sun, G., Wu, E.: Squeeze-and-excitation networks. ar**v:1709.01507 (2017).
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). (2016) https://doi.org/10.1109/cvpr.2016.90.
Han, S., Mao, H., William, J.: Deep compression: compressing deep neural networks with pruning, trained quantization and Huffman coding. ar**v:1510.00149 (2015).
Jiang, J., Cao, P., Lu, Z., Lou, W., Yang, Y.: Surface defect detection for mobile phone back glass based on symmetric convolutional neural network deep learning. Appl. Scl-Basel. (2020). https://doi.org/10.3390/app10103621
Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. Commun. ACM 60(6), 84–90 (2017). https://doi.org/10.1145/3065386
Kopparapu, S.: Lighting design for machine vision application. Image Vision Comput. 24(7), 720–726 (2006). https://doi.org/10.1016/j.imavis.2005.12.016
Lu, F., Zhang, Z., Guo, L., Chen, J., Zhu, Y., Yan, K., Zhou, X.: HFENet: a lightweight hand-crafted feature enhanced CNN for ceramic tile surface defect detection. Int. J. Intell. Syst. 37(12), 10670–10693 (2022). https://doi.org/10.1002/int.22935
Li, M., Zhang, H., **, Y., Wang, Z., Guo, G.: Parallelizing Hartley transform with Hadoop for fast detection of glass defects. Concurr. Comp-Pract. E. 30(24), e4499 (2018). https://doi.org/10.1002/cpe.4499
Lin, T., Priya, G., Ross, G., Kaiming, H Piotr, D.: Focal loss for dense object detection. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2980–2988. (2017) ar**v:1708.02002.
Miao, P., **ong, G., Ma, S., Srimahachota, T.: Deep learning-based inspection data mining and derived information fusion for enhanced bridge deterioration assessment. J. Bridge. Eng. 28(8), 04023048 (2023). https://doi.org/10.1061//JBENF2.BEENG-6053
Ming, W., Shen, F., Li, X., Zhang, Z., Du, J., Chen, Z., Cao, Y.: A comprehensive review of defect detection in 3C glass components. Measurement 158, 107722 (2020). https://doi.org/10.1016/j.measurement.2020.107722
Neogi, N., Mohanta, D.K., Dutta, P.K.: Review of vision-based steel surface inspection systems. Eurasip J Image Vide (2014). https://doi.org/10.1186/1687-5281-2014-50
Passos, A., Cousseau, T., Luersen, M.: A smart deep convolutional neural network for real-time surface inspection. Comput. Syst. Sci. Eng. 41(2), 583–593 (2022). https://doi.org/10.32604/csse.2022.02002
Subir, K., Subeesh, A., Kumkum, D., Dilip, J., Narendra, S.: Development of an optimally designed real-time automatic citrus fruit grading–sorting machine leveraging computer vision-based adaptive deep learning model. Eng. Appl. Artif. Intel. 120, 105826 (2023). https://doi.org/10.1016/j.engappai.2023.105826
Surovi, N., Soh, G.: Acoustic feature based geometric defect identification in wire arc additive manufacturing. Virtual. Phys. Prototy. 18(1), e2210553 (2023). https://doi.org/10.1080/17452759.2023.2210553
Sun, H., Zhang, S., Ren, R., Sun, L.: Surface defect detection of “Yuluxiang” pear using convolutional neural network with class-balance loss. Agronomy-Basel. 12(9), 2076 (2022). https://doi.org/10.3390/agronomy12092076
Song, K., Yan, Y.: A noise robust method based on completed local binary patterns for hot-rolled steel strip surface defects. Appl. Surf. Sci. 285, 858–864 (2013). https://doi.org/10.1016/j.apsusc.2013.09.002
Tai, Y., Yang, J., Liu, X., Xu, C.: MemNet: a persistent memory network for image restoration. ar**v:1708.02209 (2017).
Van, D., **a, J., Jeong, Y., Yoon, J.: An automatic machine vision-based algorithm for inspection ofhardwood flooring defects during manufacturing. Eng. Appl. Artif. Intel. 123A, 10628 (2023). https://doi.org/10.1016/j.engappai.2023.106268
Xu, W., **ao, P., Zhu, L., Zhang, L., Zhang, Y., Chang, J., Zhu, R., Xu, Y.: Classification and rating of steel scrap using deep learning. Eng. Appl. Artif. Intel. 123A, 106241 (2023). https://doi.org/10.1016//j.engappai.2023
Ye, S., Wang, Z., **ong, P., Xu, X., Du, L., Tan, J., Wang, W.: Multi-stage few-shot micro-defect detection of patterned OLED panel using defect inpainting and multi-scale Siamese neural network. J. Inteil. Manuf. (2023). https://doi.org/10.1007/s10845-023-02168-3
Yan, C., Zhang, G., Chen, Y., Huang, S., Zhao, Y., Wang, J.: One-dimensional structure reparameterized convolutional neural network for two-phase image reconstruction based on ERT Meas. Sci. Technol. 34(10), 105402 (2023). https://doi.org/10.1088/1361-6501/ace2df
Yu, S., Seo, D., Paik, J.: Haze removal using deep convolutional neural network for Korea Multi-Purpose Satellite-3A (KOMPSAT-3A) multispectral remote sensing imagery. Eng. Appl. Artif. Intel. 123c, 106482 (2023). https://doi.org/10.1016/j.engappai.2023.106481
Yang, S., Lee, G., Huang, L.: Deep learning-based dynamic computation task off loading for mobile edge computing networks. Sensors. 22(11), 4088 (2022). https://doi.org/10.3390/s22114088
Zhang, Z., Zhou, M., Wan, H., Li, M., Li, G., Han, D.: IDD-Net: industrial defect detection method based on deep-learning. Eng. Appl. Artif. Intel. 123B, 106390 (2023). https://doi.org/10.1016/j.engappai.2023.106390
Zhang, C., Zhang, L.: Wind turbine pitch bearing fault detection with Bayesian augmented temporal convolutional networks. Struct. Hlth. Monit. 10, 1–12 (2023). https://doi.org/10.1177/14759217231175886
Zhao, C., Shu, X., Yan, X., Zuo, X., Zhu, F.: RDD-YOLO: a modified YOLO for detection of steel surface defects. Measurement 214, 112776 (2023). https://doi.org/10.1016/j.measurement.2023.112776
Zhen, J., Li, J., Ding, Z., Kong, L., Chen, Q.: Recognition of expiry data on food packages based on improved DBNet. Connect. Sci. 35(1), 1–16 (2023). https://doi.org/10.1080/09540091.2023.2202363
Zhang, J., Qian, S., Tan, C.: Automated bridge surface crack detection and segmentation using computer vision-based deep learning model. Eng. Appl. Artif. Intel. 115, 105225 (2022). https://doi.org/10.1016/j.engappai.2022.105225
Zhang, X., Zhou, X., Lin, M., Sun, J.: ShuffleNet: an extremely efficient convolutional neural network for mobile devices. ar**v:1707.01083 (2017).
Acknowledgements
This work was supported by the Tongling University Industry-Academia-Research(IAR) Horizontal Research Project (Nos. 2022tlxyxdz118, 2023tlxyxdz019).
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JX contributed to ideas, creation of models, writing–original draft, and software. ZH contributed to formulation or evolution of overarching research goals and aims, provision of study materials, reviewing and editing. QZ contributed to supervision, management and coordination responsibility for the planning and execution of research activities. RY contributed to software.
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**ong, J., He, Z., Zhou, Q. et al. Photovoltaic glass edge defect detection based on improved SqueezeNet. SIViP 18, 2841–2856 (2024). https://doi.org/10.1007/s11760-023-02954-9
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DOI: https://doi.org/10.1007/s11760-023-02954-9