Recognition and sorting of coal and gangue based on image process and multilayer perceptron

Abstract

The recognition and sorting of coal and gangue are an important part of the process of reducing costs and improving production efficiency. In this paper, under the influence of long running pollution and similar background grayscale, a new method was proposed based on image process and multilayer perceptron. The robust image extraction method was used for the reverse selection edge extraction method (RS-EEM) to eliminate background interference. Moreover, a deep learning model called multilayer perceptron (MLP) was proposed to recognize coal and gangue with simple structure, which is easy to transplant with high precision and great timeliness. In order to evaluate the correctness and efficiency of the algorithm, a sorting robot of coal and gangue was built based on MLP and image processing. The experiment results showed that the images of coal and gangue can be precisely recognized based on RS-EEM under the influence of long running pollution and similar background grayscale, and the recognition accuracy can reach to 96.15% as well as grasping accuracy to 85% at 0.4 m/s conveyor belt speed.