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Application of detection and tracking for equipments in open-pit mines based on YOLOv8n+DeepSORT technology

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2024, 10(5), pp.235~252
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : August 19, 2024
  • Accepted : October 11, 2024
  • Published : October 31, 2024

Li Ke 1 민병원 2

1목원대학교 정보통신융합공학부
2목원대학교 정보통신융합공학과

Accredited

ABSTRACT

To address the inefficiencies of visual interpretation for mining equipment supervision in open-pit mines, this study proposes an enhanced YOLOv8n+DeepSORT framework. By leveraging high-point surveillance for image acquisition and optimizing both YOLOv8n for equipment identification and DeepSORT for real-time tracking, we overcome limitations in accuracy, cost, and real-time monitoring. Field validation at Jingkai Runding Mine, Pingxiang, Jiangxi, demonstrates the technology's efficacy in identifying and tracking mining equipment, featuring rapid algorithm convergence, low computational overhead, and near-precise target detection and tracking. This approach paves the way for algorithmic support to facilitate effective government regulation of open-pit mining operations.

Citation status

* References for papers published after 2023 are currently being built.