Affiliation:
1. School of Mechanical and Precision Instrument Engineering, Xi’an University of Technology, Xi’an 710048, China
2. School of Chemical and Environmental Engineering, China University of Mining & Technology-Beijing, Beijing 100083, China
Abstract
Coal gangue image recognition is a critical technology for achieving automatic separation in coal processing, characterized by its rapid, environmentally friendly, and energy-saving nature. However, the response characteristics of coal and gangue vary greatly under different illuminance conditions, which poses challenges to the stability of feature extraction and recognition, especially when strict illuminance requirements are necessary. This leads to fluctuating coal gangue recognition accuracy in industrial environments. To address these issues and improve the accuracy and stability of image recognition under variable illuminance conditions, we propose a novel coal gangue recognition method based on laser speckle images. Firstly, we studied the inter-class separability and intra-class compactness of the collected laser speckle images of coal and gangue by extracting gray and texture features from the laser speckle images, and analyzed the performance of laser speckle images in representing the differences between coal and gangue minerals. Subsequently, coal gangue recognition was achieved using an SVM classifier based on the extracted features from the laser speckle images. The fusion feature approach achieved a recognition accuracy of 94.4%, providing further evidence of the feasibility of this method. Lastly, we conducted a comparative experiment between natural images and laser speckle images for coal gangue recognition using the same features. The average accuracy of coal gangue laser speckle image recognition under various lighting conditions is 96.7%, with a standard deviation of the recognition accuracy of 1.7%. This significantly surpasses the recognition accuracy obtained from natural coal and gangue images. The results showed that the proposed laser speckle image features can facilitate more stable coal gangue recognition with illumination factors, providing a new, reliable method for achieving accurate classification of coal and gangue in the industrial environment of mines.
Funder
National Natural Science Foundation of China
Key Research and Development Plan Project in Shaanxi Province
Xi’an Science and Technology Plan Project
Subject
Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry
Reference45 articles.
1. Study of on-line recognition and automatic separation of waste rock in coal mine;Ma;J. Xi’an Univ. Sci. Technol.,2003
2. Research on green mining of coal resources in China: Current status and future prospects;Miao;J. Min. Saf. Eng.,2009
3. Ma, X.M. (2009, January 10–11). Coal Gangue Image Identification and Classification with Wavelet Transform. Proceedings of the 2009 Second International Conference on Intelligent Computation Technology and Automation, Changsha, China.
4. Development and prospect on fully mechanized mining in Chinese coal mines;Wang;Int. J. Coal Sci. Technol.,2014
5. Research status and prospect of coal gangue identification method;Cao;Ind. Mine Autom.,2020
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献