Defective Fiducial Mark Detection Using Machine Learning
Do Gyu An, Jung Won Jung, Jae Wook Jeon · 2018
In this paper, we propose a method to improve the performance of the fiducial mark detection function using a vision sensor in automation equipment. In the automation industry, Template matching method is used to recognize the fiducial mark. Template matching can be detected because the error increases when the mark of the target rotates more than a certain angle. If the mark is damaged due to illumination and a physical external force, there is a reduction in the recognition rate. Therefore, we propose a method consisting of K-means Clustering, SVM Classification, and Linear Regression. Using the proposed method, the recognition rate of the fiducial mark is improved and accurate center coordinates are obtained.