Research on mine underground inspection robot target detection algorithm based on pyramid structure and attention mechanism coupling

被引：0
|
作者：
Wang, Maosen [1 ]
Bao, Jiusheng [1 ]
Bao, Zhouyang [1 ]
Yin, Yan [1 ]
Wang, Xiangsai [1 ]
Ge, Shirong [2 ]
机构：
[1] School of Mechanical and Electrical Engineering, China University of Mining and Technology, Xuzhou,221116, China
[2] School of Mechanical and Electrical Engineering, China University of Mining and Technology-Beijing, Beijing,100083, China
来源：
Meitan Kexue Jishu/Coal Science and Technology (Peking) | 2024年 / 52卷 / 06期
关键词：
In recent years; coal mine robots have become a research hotspot in the field of modern coal machine equipment; and the main coal flow transportation system of most coal mines has basically realized continuity; mechanization and automation; which also puts forward higher requirements for safety monitoring and inspection efficiency in the main transportation roadway; and accurate target detection is a necessary guarantee for intelligent safety monitoring in coal mines; but the existing object detection algorithm is applied to complex and harsh coal mine underground roadway environment; and there is a problem of low target detection accuracy. Aiming at the special working condition detection requirements of low lighting and chaotic environment in the downhole; the target data set in the underground roadway environment was produced; and the dataset annotation was completed and multi-dimensional analysis was carried out. A PT target detection algorithm based on the fusion of pyramid structure and attention mechanism is proposed; and the attention mechanism module is used to replace the convolution module in the pyramid structure; which improves the extraction ability of global features while controlling the amount of feature calculation; realizes the extraction effect of the fusion of local features and global features of the target; and improves the expression ability of the features of the target area of interest in the image. Finally; for the application scenario of underground inspection robot in coal mine; the proposed PT algorithm is compared with the traditional Faster R-CNN and YOLOv4 algorithms. Compared with the mainstream Faster R-CNN and YOLOv4 target detection networks; the PT algorithm has better comprehensive recognition capabilities; and the accuracy of identifying coal mine personnel is increased by 2.90% and 4.30%; the accuracy of identifying underground obstacles is increased by 0.20% and 4.80%; and the accuracy of identifying mine cracks is increased by 4.40% and 8.60%; respectively. The accuracy rate of identifying downhole equipment was improved by 3.00% and 8.70%; respectively; Therefore; the PT target detection algorithm can better adapt to the underground environment; and the target detection algorithm can obtain higher accuracy and detection speed than other algorithms; which can provide theoretical basis and technical support for the construction of underground roadway security control system. © 2024 China Coal Society. All rights reserved;
D O I：
10.12438/cst.2023-1071
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页码：206 / 215
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