Volume 48 Issue 10
Oct.  2022
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Article Navigation >  Journal of Mine Automation  > 2022  >  48(10): 69-75
ZHANG Dong, JIANG Yuanyuan. Drill pipe counting method based on improved MobileNetV2[J]. Journal of Mine Automation，2022，48(10)：69-75.  doi: 10.13272/j.issn.1671-251x.2022060019
Citation: ZHANG Dong, JIANG Yuanyuan. Drill pipe counting method based on improved MobileNetV2[J]. Journal of Mine Automation，2022，48(10)：69-75.  doi: 10.13272/j.issn.1671-251x.2022060019
shu

Drill pipe counting method based on improved MobileNetV2

doi: 10.13272/j.issn.1671-251x.2022060019
  • 1.

    School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China

  • 2.

    Institute of Environment-friendly Materials and Occupational Health, Anhui University of Science and Technology, Wuhu 241003, China

  • Received Date: 2022-06-07
  • Rev Recd Date: 2022-10-07
    • Available Online: 2022-09-19
    Abstract
  • The existing drill pipe counting methods based on manual and instrument have the problems of low precision, time-consuming and labor-consuming. The existing drill pipe counting methods based on image processing are difficult to extract image features, the network model has high complexity and large amount of computation. In order to solve the above problems, a drill pipe counting method based on improved MobileNetV2 is proposed. The working state image of the drilling rig is collected through a camera. The collected image is preprocessed by adopting data enhancement. On the basis of MobileNetV2, the convolutional block attention module is added to enhance the thinning capability of features. The objective function is optimized to improve the recognition precision. The initial parameters are obtained through transfer learning. The improved MobileNetV2 is used as the working state recognition model of the drilling rig. The working state features of the drilling rig are extracted by the model. The confidence data are generated by recognizing the four working states of the drilling rig, including drill pipe installation, drill pipe driving, drill pipe unloading and shut down during the whole drilling process of the drill pipe. The confidence data are filtered through a sliding window. The number of drill pipes is accurately counted, and the drilling depth is determined. The experimental results show that the recognition accuracy of the improved MobileNetV2 model reaches 99.95%. Compared with the classical classification models ResNet50, Xception, InceptionV3, InceptionResNetV2 and MobileNetV2, the accuracy is improved by 1.35%, 1.28%, 1.43%, 0.85% and 1.25% respectively. The parameter is reduced by 38.9% compared with the MobileNetV2 model. The convergence speed of the model is faster and the comprehensive performance is better. The drill pipe counting method based on the improved MobileNetV2 is applied to the drill pipe counting of fully mechanized mining face of a coal mine. The average drill pipe statistical precision is 98.4%. The accurate counting of the drill pipes is realized. The feasibility and practicability of application of the method in the complex environment are verified.

     

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