High Precision Edge Detection Algorithm for Mechanical Parts

Open access


High precision and high efficiency measurement is becoming an imperative requirement for a lot of mechanical parts. So in this study, a subpixel-level edge detection algorithm based on the Gaussian integral model is proposed. For this purpose, the step edge normal section line Gaussian integral model of the backlight image is constructed, combined with the point spread function and the single step model. Then gray value of discrete points on the normal section line of pixel edge is calculated by surface interpolation, and the coordinate as well as gray information affected by noise is fitted in accordance with the Gaussian integral model. Therefore, a precise location of a subpixel edge was determined by searching the mean point. Finally, a gear tooth was measured by M&M3525 gear measurement center to verify the proposed algorithm. The theoretical analysis and experimental results show that the local edge fluctuation is reduced effectively by the proposed method in comparison with the existing subpixel edge detection algorithms. The subpixel edge location accuracy and computation speed are improved. And the maximum error of gear tooth profile total deviation is 1.9 μm compared with measurement result with gear measurement center. It indicates that the method has high reliability to meet the requirement of high precision measurement.

[1] Kumar, B.M, Ratnam, M.M. (2015). Machine vision method for non-contact measurement of surface roughness of a rotating workpiece. Sensor Review, 35 (1), 10-19.

[2] Gadelmawla, E.S. (2011). Computer vision algorithms for measurement and inspection of spur gears. Measurement, 44 (9), 1669-1678.

[3] Kosarevsky, S., Latypov, V. (2013). Detection of screw threads in computed tomography 3D density fields. Measurement Science Review, 17 (2), 93-99.

[4] Robinson, M.J., Oakley, J.P., Cunningham, M.J. (1995). The accuracy of image analysis methods in spur gear metrology. Measurement Science and Technology, 6, 860-871.

[5] Chen, F., Brown, G.M. (2000). Overview of threedimensional shape measurement using optical methods. Optical Engineering, 39 (1), 10-21.

[6] Lu, N.-G., Deng, W.-Y., Wang, Y.-Q. (2005). Profile measurement of microwave antenna using close range photogrammetry. In Third International Conference on Experimental Mechanics and Third Conference of the Asian Committee on Experimental Mechanics, Proc. SPIE 5852, 508-515.

[7] Sidor, K., Szlachta, A. (2017). The impact of the implementation of edge detection methods on the accuracy of automatic voltage reading. Measurement Science Review, 13 (6), 292-297.

[8] Shang, Y.-C., Chen, J., Tian, J.-W. (2010). The study of sub-pixel edge detection algorithm based on the function curve fitting. In 2nd International Conference on Information Engineering and Computer Science. IEEE, 1-4.

[9] Wei, B.-Z., Zhao, Z.-M. (2013). A sub-pixel edge detection algorithm based on Zernike moments. The Imaging Science Journal, 61, 436-446.

[10] Liu, G., Liu, B., Chen, F., Hu, T. (2009). Study on the method of the accuracy evaluation of sub-pixel location operators. Acta Optica Sinica, 29, 3446-3451.

[11] Li, S., Lu, R., Shi, Y. et al. (2011). Sub-pixel edge detection algorithm based on Gaussian Surface Fitting. Tool Engineer, 45, 79-82.

[12] Ma, R., Zeng, L., Lu, Y. (2009). Improved sub-pixel edge detection based on Facet model. Journal of Basic Science and Engineering, 17, 296-302.

[13] Wang, K., Zhang, D., Huang, H. et al., (2005). A study of sub voxel edge detection method based on 3-D Facet model. Mechanical Science and Technology, 24, 865-868.

[14] Xu, L.-Y., Cao, Z.-Q., Zhao, P., Zhou, C. (2017). A new monocular vision measurement method to estimate 3D positions of objects on floor. International Journal of Automation and Computing, 14, 159-168.

[15] Yu, Q.-F., Shang, Y. (2009). Videometrics: Principles and Research. Beijing, China: Science Press.

[16] He, Z., Wang, B. (2003). Sub-pixel extraction algorithm using curve fitting method. Journal of Scientific Instrument, 24, 195-197.

[17] Chang, S.-T., Sun, Z.-Y., Zhang, Y.-Y., Zhu, W. (2014). Radiation measurement of small targets based on PSF. Optics and Precision Engineering, 22 (11), 2879-2887.

[18] Duan, Z.-Y., Wang, N., Zhao, W.-H. et al. (2016). Study on calibration method based on lattice calibration plate in vision measurement system. Acta Optica Sinica, 36 (5), 0515004.

[19] Fei, Z.-G, Xu, X.-J., Anthimos, G. (2016). Short-arc measurement and fitting based on the bidirectional prediction of observed data. Measurement Science and Technology, 27 (2), 025013.

Measurement Science Review

The Journal of Institute of Measurement Science of Slovak Academy of Sciences

Journal Information

IMPACT FACTOR 2017: 1.345
5-year IMPACT FACTOR: 1.253

CiteScore 2017: 1.61

SCImago Journal Rank (SJR) 2017: 0.441
Source Normalized Impact per Paper (SNIP) 2017: 0.936


All Time Past Year Past 30 Days
Abstract Views 0 0 0
Full Text Views 97 97 15
PDF Downloads 53 53 8