Research on online visual recognition of coal gangue based on BLOB analysis and support vector machine

Article type: Research Article

Authors: Li, Zuming^* | Zou, Huadong

Affiliations: College of Electromechanical and Automotive Engineering, Qingyuan Polytechnic, Qingyuan, Guangdong, China

Correspondence: [*] Corresponding author: Zuming Li, College of Electromechanical and Automotive Engineering, Qingyuan Polytechnic, Qingyuan, Guangdong, China. E-mail: [email protected].

Abstract: In order to solve the problem of real-time and accurate recognition of coal gangue in the intelligent separation system of coal gangue, an online visual recognition algorithm of coal gangue based on BLOB analysis and machine learning is proposed. It filters the easily recognized gangue or coal by triple filter model with small calculation, which only discriminating the suspected gangue image extremely difficult to recognize. The remaining small amount of suspected coal gangue image is distinguished by calculating the local characteristic parameters and inputting them into the SVM classification model. The algorithm has been applied to the intelligent sorting system of coal gangue and verified by experiments. The test results show that it improves the recognition rate of coal gangue and ensures the real-time detection.

Keywords: BLOB analysis, machine learning, support vector machine, coal gangue identification

DOI: 10.3233/JCM-247236

Journal: Journal of Computational Methods in Sciences and Engineering, vol. 24, no. 4-5, pp. 2123-2134, 2024