Design of a diamond adsorption detection system based on machine learning techniques
Zhun Fan, Youxiang Zuo, Fang Li, Shuangxi Wang · 2016
A diamond adsorption detecting system based on machine learning is presented in this paper. The paper describes the system from the perspective of hardware and software design, and presents the image processing and machine learning algorithms applied in the system. The hardware includes three major parts - the camera, light source and support platform. The software includes modules of image acquisition, image preprocessing, feature extraction, and machine learning. This paper utilizes three supervised machine learning algorithms, namely Support Vector Machine (SVM), Classification and Regression Tree (CART) and C4.5 decisions. Through the comparison study of the three algorithms, SVM is found to have the best performance for this system. It is demonstrated in experimental tests that the algorithm can obtain an accuracy of 97.84%, which improves the detection efficacy of the system significantly.