Education, Science, Technology, Innovation and Life
Open Access
Sign In

Research on Grassland Demarcation Line Extraction Based on Improved Least Squares Method

Download as PDF

DOI: 10.23977/jipta.2019.21001 | Downloads: 4 | Views: 126

Author(s)

Yin Yalin 1, Hu Lianjun 2, Song Hong 3

Affiliation(s)

1 School of Automation and Information Engineering, Sichuan University of Science and Technology. Yibin City, Sichuan Province, China
2 School of Mechanical Engineering, Sichuan University of Science and Technology. Yibin City, Sichuan Province, China
3 Sichuan University of Science and Technology. Yibin City, Sichuan Province, China

Corresponding Author

Yin Yalin

ABSTRACT

The rapid development of mobile robots has made intelligent mowing robots widely concerned. The dividing line between grassland and non-grass is an important reference for robots to carry out mowing navigation.Aiming at this problem, this paper proposes a grassland boundary line extraction algorithm based on improved least squares method based on image segmentation binary image.Firstly, the corner point in the image is detected by the Shi-Tomasi algorithm, and the approximate range of the grass boundary line is obtained. Then, the modified corner point is fitted by the improved least squares method, and finally the boundary line between the grassland and the non-grass is obtained. Finally, three straight line fitting methods were compared by experiments. The results show that the improved least squares method has the best effect and the fastest running speed.

KEYWORDS

Intelligent mowing robot; Corner detection; Straight line fitting; Least squares

CITE THIS PAPER

Yin Yalin, Hu Lianjun and Song Hong, Research on Grassland Demarcation Line Extraction Based on Improved Least Squares Method, Journal of Image Processing Theory and Applications (2019) Vol. 2: 1-7. DOI: http://dx.doi.org/10.23977/jipta.2019.21001.

REFERENCES

[1] Mahoney, R.T. (1994) Dividing Lines: Canals, Railroads, and Urban Rivalry in Ohio's Hocking Valley, Journal of American History, 1995, 82(3):1205.
[2] Zhao, W.J, Gong, S.R. and Liu, C.P. (2008) Adaptive Harris Corner Detection Algorithm. Computer Engineering, 34(10):212-214.
[3] Wu, M., Ramakrishnan, N., Lam and S.K. (2012) Low-complexity pruning for accelerating corner detection[C]// IEEE International Symposium on Circuits & Systems. 
[4] Xiong, J., Tian, S. and Yang, C. (2017) Fault modeling on complex field using least‐square circle fitting for linear analog circuits. IEEE Transactions on Electrical & Electronic Engineering, 12(11).
[5] Bao, Y., Jin, J.S. and Fei, L. (2017) A perspective correction method based on the bounding rectangle and least square fitting. International Computer Conference on Wavelet Active Media Technology & Information Processing. 
[6] Ram, P. and Padmavathi, S. (2017) Analysis of Harris corner detection for color images. International Conference on Signal Processing. 
[7] Li, G., Ran, Z. and Chai, H. (2017) A contour detector with improved corner detection. Multimedia Tools & Applications, 76(4):1-20.
[8] Zhanli, L.I., Chen, J. and Hongan, L.I. (2017) Research on Intelligent Monitoring and Warning Method of Belt Conveyor. Journal of Graphics.

Downloads: 313
Visits: 12075

Sponsors, Associates, and Links


All published work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright © 2016 - 2031 Clausius Scientific Press Inc. All Rights Reserved.