Main > Acta Optica Sinica >  Volume 40 >  Issue 24 >  Page 2411001 > Article
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  • Received: Jun. 28, 2020

    Accepted: Sep. 8, 2020

    Posted: Dec. 1, 2020

    Published Online: Nov. 23, 2020

    The Author Email: Zhou Mengran (mrzhou8521@163.com)

    DOI: 10.3788/AOS202040.2411001

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    Wenhao Lai, Mengran Zhou, Feng Hu, Kai Bian, Hongping Song. Coal Gangue Detection Based on Multi-Spectral Imaging and Improved YOLO v4[J]. Acta Optica Sinica, 2020, 40(24): 2411001

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Acta Optica Sinica, Vol. 40, Issue 24, 2411001 (2020)

Coal Gangue Detection Based on Multi-Spectral Imaging and Improved YOLO v4

Lai Wenhao, Zhou Mengran*, Hu Feng, Bian Kai, and Song Hongping

Author Affiliations

  • College of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan, Anhui 232000, China

Abstract

The separation of coal gangue from coal is of great significance for environmental protection and resource-saving. Therefore, this article proposes an intelligent separation method for coal gangue based on multi-spectral imaging technology and object detection. First, a multi-spectral data acquisition system for coal and coal gangue is set up in the laboratory, and 850 groups of multispectral data are collected. Second, by studying the coal gangue recognition rate and the correlation of each band of multi-spectral data, three bands from 25 bands are selected to form a pseudo-RGB (Red, Green, and Blue) image. Finally, the improved object detection model YOLO v4.1 is used to detect coal gangue. Experimental results show that the the mean average precision of YOLO v4.1 for coal and coal gangue detection on the test set is 98.26%, and the detection time is about 4.18 s. The method can not only precisely identify coal and coal gangue, but also obtain their relative position and size, which is important for the seperation operation of coal gangue.

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