Proceedings of the 2016 International Conference on Computer Engineering and Information Systems

Vision-based Lawn Boundary Recognition for Mowing Robot

Authors
Yao Guo, Fu-Chun Sun
Corresponding Author
Yao Guo
Available Online November 2016.
DOI
https://doi.org/10.2991/ceis-16.2016.16How to use a DOI?
Keywords
component; mowing robot; computer vision; gabor filter; PCA; SVM
Abstract
We present a novel method for mowing robot's lawn boundary recognition based on Gabor filters and support vector machine (SVM). Robust texture features of images are extracted and concatenated using Gabor filters. The principle components analysis (PCA) approach is then used to reduce the dimensionality of Gabor features. Based on the compressed features, SVM model is trained and used to perform the grass texture classification task. The boundary of lawn is then recognized according to the ratio of grass area of the image. To demonstrate the effectiveness and robustness of our proposed method, a dataset is created with about 1500 images of different lawn scenes. Result shows that a classification accuracy of 96.7% can be reached when SVM is used. Experiments of the lawn boundary recognition have also been conducted on the mowing robot under different lighting conditions. The recognition rate tested is 98.3%, which proves the efficiency and superiority of our proposed method.
Open Access
This is an open access article distributed under the CC BY-NC license.

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Proceedings
2016 International Conference on Computer Engineering and Information Systems
Part of series
Advances in Computer Science Research
Publication Date
November 2016
ISBN
978-94-6252-283-1
ISSN
2352-538X
DOI
https://doi.org/10.2991/ceis-16.2016.16How to use a DOI?
Open Access
This is an open access article distributed under the CC BY-NC license.

Cite this article

TY  - CONF
AU  - Yao Guo
AU  - Fu-Chun Sun
PY  - 2016/11
DA  - 2016/11
TI  - Vision-based Lawn Boundary Recognition for Mowing Robot
BT  - 2016 International Conference on Computer Engineering and Information Systems
PB  - Atlantis Press
SN  - 2352-538X
UR  - https://doi.org/10.2991/ceis-16.2016.16
DO  - https://doi.org/10.2991/ceis-16.2016.16
ID  - Guo2016/11
ER  -