Intelligent Identification Method of Shearer Drums Based on Improved YOLOv5s with Dark Channel-Guided Filtering Defogging

Qinghua Mao, Menghan Wang (), Xin Hu, Xusheng Xue and Jiao Zhai
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Qinghua Mao: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Menghan Wang: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Xin Hu: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Xusheng Xue: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China
Jiao Zhai: School of Mechanical Engineering, Xi’an University of Science and Technology, Xi’an 710054, China

Energies, 2023, vol. 16, issue 10, 1-15

Abstract: In a fully mechanized mining face, there is interference between the hydraulic support face guard and the shearer drum. The two collisions seriously affect coal mine production and personnel safety. The identification of a shearer drum can be affected by fog generated when the shearer drum cuts forward. It is hydraulic support face guard recovery, not the timely block shearer drum, that will also affect the recognition of the shearer drum. Aiming at the above problems, a shearer drum identification method based on improved YOLOv5s with dark channel-guided filtering defogging is proposed. Aiming at the problem of fog interference affecting recognition, the defogging method for dark channel guided filtering is proposed. The optimal value of the scene transmittance function is calculated using guided filtering to achieve a reasonable defogging effect. The Coordinate Attention (CA) mechanism is adopted to improve the backbone network of the YOLOv5s algorithm. The shearer drum features extracted by the C3 module are reallocated by the attention mechanism to the weights of each space and channel. The information propagation of a shearer drum’s features is enhanced by such improvements. Thus, the detection of shearer drum targets in complex backgrounds is improved. S Intersection over Union (SIoU) is used as a loss function to improve the speed and accuracy of the shearer drum. To verify the effectiveness of the improved algorithm, multiple and improved target detection algorithms are compared. The algorithm is deployed at Huangling II mine. The experimental results present that the improved algorithm is superior to most target detection algorithms. In the absence of object obstruction, the improved algorithm achieved 89.3% recognition accuracy and a detection speed of 48.8 frame/s for the shearer drum in the Huangling II mine. The improved YOLOv5s algorithm provides a basis for identifying interference states between the hydraulic support face guard and shearer drum.

Keywords: fully mechanized mining face; YOLOv5s; shearer drum; intelligent identification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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