Volume 48 Issue 4
Apr.  2022
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ZHANG Yixiang, LIN Song, LI Xue. Personnel detection in dangerous area of coal preparation plant based on CenterNet-GhostNet[J]. Journal of Mine Automation，2022，48(4)：66-71. DOI: 10.13272/j.issn.1671-251x.2021080058
Citation: ZHANG Yixiang, LIN Song, LI Xue. Personnel detection in dangerous area of coal preparation plant based on CenterNet-GhostNet[J]. Journal of Mine Automation，2022，48(4)：66-71. DOI: 10.13272/j.issn.1671-251x.2021080058
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Personnel detection in dangerous area of coal preparation plant based on CenterNet-GhostNet

  • School of Mechanical Electronic and Information Engineering, China University of Mining and Technology(Beijing), Beijing 100083, China

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  • Received Date: August 20, 2021
  • Revised Date: April 01, 2022
  • Available Online: March 04, 2022
    Graphical Abstract
    • Abstract
  • Due to the dust and fog interference, it is difficult to distinguish accurately the whole body target of personnel in dangerous areas of coal preparation plant from the production environment background. Moreover, the head features of personnel are relatively easy to be identified, and the possibility of head being blocked in the monitoring perspective is low. Therefore, the head detection of personnel in dangerous areas is used instead of the whole body target detection of personnel. At present, the lightweight target detection model based on deep learning has a lot of information loss in feature extraction, and its detection capability of human head target is limited. In order to solve this problem, a lightweight personnel detection model CenterNet-GhostNet is proposed. The model takes CenterNet network as the basic framework, and combines the lightweight network GhostNet and the feature pyramid network(FPN) as the feature extraction network. GhostNet extracts the features of the input image and improves the network feature expression capability. And the FPN fuses the information contained in the feature maps with different resolutions extracted by GhostNet, so that more detailed information is reserved while extracting the high-level semantic features. Three independent convolution operation branches are used to decode and calculate the single output feature map with higher resolution, so as to make full use of the detailed information contained in the feature map. The experimental results show that the detection precision of CenterNet-GhostNet model is 93.7% and 91.7% respectively for the two types of head targets with and without helmet, which are better than the general lightweight models SSD-MobileNet, YOLOv4 Tiny and CenterNet-Res18. The single frame detection time of CenterNet-GhostNet model deployed on NVIDIA Jetson Nano is 67 ms, which meets the requirements of high-precision and real-time detection of personnel in dangerous areas of coal preparation plant.
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    • References (14)
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