Journal of Information Processing Systems

Korea Information Processing Society (한국정보처리학회)
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Defect Detection of Steel Wire Rope in Coal Mine Based on Improved YOLOv5 Deep Learning

  • Xiaolei Wang (School of Information and Control Engineering, Liaoning Petrochemical University) ;
  • Zhe Kan (School of Information and Control Engineering, Liaoning Petrochemical University)
  • Received : 2023.01.16
  • Accepted : 2023.06.06
  • Published : 2023.12.31
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Abstract

The wire rope is an indispensable production machinery in coal mines. It is the main force-bearing equipment of the underground traction system. Accurate detection of wire rope defects and positions exerts an exceedingly crucial role in safe production. The existing defect detection solutions exhibit some deficiencies pertaining to the flexibility, accuracy and real-time performance of wire rope defect detection. To solve the aforementioned problems, this study utilizes the camera to sample the wire rope before the well entry, and proposes an object based on YOLOv5. The surface small-defect detection model realizes the accurate detection of small defects outside the wire rope. The transfer learning method is also introduced to enhance the model accuracy of small sample training. Herein, the enhanced YOLOv5 algorithm effectively enhances the accuracy of target detection and solves the defect detection problem of wire rope utilized in mine, and somewhat avoids accidents occasioned by wire rope damage. After a large number of experiments, it is revealed that in the task of wire rope defect detection, the average correctness rate and the average accuracy rate of the model are significantly enhanced with those before the modification, and that the detection speed can be maintained at a real-time level.

Keywords

Acknowledgement

This paper is supported by Liaoning Natural Science Foundation (2020019), China.

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