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Research on detection and location of weak edge signals

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Abstract

In order to be able to control the growth rate of industrial polycrystalline silicon rods well and reduce energy consumption, it is necessary to accurately locate the boundaries of the silicon rods and measure the diameter of the silicon rod during growth. In this paper, a weak edge signal detection method based on small area is proposed for the problem of weak boundary signal in the late growth stage of polycrystalline silicon rods. The method increases the gradient of the edge by projecting the boundary signal in the column direction. In addition, the method further enhances the gradient information of the weak boundary signal by improving the classical difference operator. In the experimental part, the improved difference operator increases the weak difference signal value from 304 to 686. Finally, the effectiveness of the algorithm is proved by two groups of experimental results.

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References

  1. Liu, X.: Research on on-line monitoring system of silicon rod diameter in polycrystalline silicon reduction furnace. Anhui University, Hefei (2014)

    Google Scholar 

  2. Jiang, H., Cao, Z., Liu, S.: Diameter detection and deposition process of CVD polysilicon reactor. Mod. Chem. Ind. 35(3), 169–172 (2015)

    Google Scholar 

  3. Wu, H., Liu, X., Chen, X., Pang, J., Li, Z., Xiong, D.: Silicon rod diameter measurement method based on binocular vision technique. J. Atmosp. Environ. Opt. 9(5), 401–408 (2014)

    Google Scholar 

  4. Bhardwaj, S., Mittal, A.: A survey on various edge detector techniques. Proc. Technol. 4, 220–226 (2012)

    Article  Google Scholar 

  5. Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 125(1–3), 3–18 (2017)

    Article  MathSciNet  Google Scholar 

  6. Liu, Y., Cheng, M.M., Hu, X., et al.: Richer convolutional features for edge detection. IEEE Trans. Pattern Anal. Mach. Intell. 41(8), 1939–1946 (2019)

    Article  Google Scholar 

  7. Deng R, Shen C, Liu S, et al. Learning to predict crisp boundaries. (2018). https://doi.org/10.1007/978-3-030-01231-1_35

  8. He, K., Sun, J., Tang, X.: Single image haze removal using dark channel prior. IEEE Trans. Pattern Anal. Mach. Intell. 33(12), 2341–2353 (2011)

    Article  Google Scholar 

  9. Sun, X., Sun, J., Zhao, L., Cao, Y.: Improved algorithm for single image haze removing using dark channel prior. J. Image Gr. 3, 381–385 (2014)

    Google Scholar 

  10. Pei S., Lee, T.: Effective image haze removal using dark channel prior and post-processing. In: 2012 IEEE International Symposium on Circuits and Systems. 2777–2780 (2012)

  11. Singh, D., Kumar, V.: Single image haze removal using integrated dark and bright channel prior. Mod. Phys. Lett. B (2018). https://doi.org/10.1142/S0217984918500513

    Article  MathSciNet  Google Scholar 

  12. Narasimhan, S., Nayar, S.: Chromatic framework for vision in bad weather. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, (CVPR2000), (2000). https://doi.org/10.1109/CVPR.2000.855874

  13. Narasimhan, S., Nayar, K.: Vision and the Atmosphere. Int. J. Comput. Vis. 48(3), 233–254 (2002)

    Article  Google Scholar 

  14. Fattal, R.: Single image dehazing. ACM Trans. Gr. (2008). https://doi.org/10.1145/1399504.1360671

    Article  Google Scholar 

  15. Tan, RT.: Visibility in bad weather from a single image. In: 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2008). 2347–2354 (2008)

  16. Yang, X., Lv, Y.: Research and implementation of grayscale image threshold segmentation based on difference operators combined with improved otsu algorithm. Instrum. Tech. Sens. 3, 104–106 (2015)

    Google Scholar 

  17. Young, N., Evans, A.N.: Median centred difference gradient operator and its application in watershed segmentation. Electron. Lett. 47(3), 178–180 (2011)

    Article  Google Scholar 

Download references

Funding

This work was supported by the National Natural Science Foundation of China under Grants No. U1830133 (NSAF).

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Correspondence to Cao Xiaohan.

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Xiuyun, Z., Xiaohan, C., Ting, Z. et al. Research on detection and location of weak edge signals. SIViP 14, 1355–1360 (2020). https://doi.org/10.1007/s11760-020-01679-3

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  • DOI: https://doi.org/10.1007/s11760-020-01679-3

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