An improved cross algorithm edge detection method and its application in coal gangue identification
Xinquan Wang, Panpan Zhao, S. J. Yue, Yu Gu
Abstract
In order to solve the problems of edge detection error, image segmentation distortion and low recognition accuracy in coal gangue separation under complex background conditions, an improved cross algorithm edge detection method is proposed in this paper, and it is applied in an example of coal gangue recognition. The cross algorithm is used to detect the edges of coal and coal gangue images, and the detection results are processed by morphological technology to obtain the segmentation results of single coal and coal gangue images. The mean value, contrast and entropy value of coal and coal gangue are extracted as recognition features to construct a support vector machine (SVM) recognition model. The accuracy of SVM identification model is 96.67% for coal and 100% for coal gangue, and it is obviously superior to K-Nearest Neighbor (KNN) identification model, Probabilistic Neural Network (PNN) identification model and Back Propagation Neural Network (BP) identification model in time. This study provides a new method to solve the identification problem of coal and gangue.