Study on the Application of Real-Time Drone Monitoring in Ordos Open-Pit Coal Mine  

作　　者：Junyi Wu Weizheng Wang Xuesong Ni Junyi Wu;Weizheng Wang;Xuesong Ni(School of Computer, North China Institute of Science and Technology, Yanjiao, China;School of Safety Engineering, North China Institute of Science and Technology, Yanjiao, China;Security Department, North China Institute of Science and Technology, Yanjiao, China)

机构地区：[1]School of Computer, North China Institute of Science and Technology, Yanjiao, China [2]School of Safety Engineering, North China Institute of Science and Technology, Yanjiao, China [3]Security Department, North China Institute of Science and Technology, Yanjiao, China

出　　处：《Open Journal of Applied Sciences》2023年第4期483-495,共13页应用科学（英文）

摘　　要：In the process of intelligent mine construction in open-pit mine, in order to improve the safety monitoring ability of mine transportation system, solve the problems of large human interference and blind Angle detection by existing conventional monitoring methods, this paper establishes an open-pit mine monitoring data set, and proposes a real-time intelligent monitoring model based on UAV. The reasoning component with strong computing power and low power consumption is selected, and the lightweight object detection model is selected for the experiment. A quantitative standard of dynamic energy consumption detection by evaluation algorithm is proposed. Through experimental comparison, it is found that YOLOv4-tiny has the highest comprehensive grade in detection accuracy, speed, energy consumption and other aspects, which is suitable for application in the above model.In the process of intelligent mine construction in open-pit mine, in order to improve the safety monitoring ability of mine transportation system, solve the problems of large human interference and blind Angle detection by existing conventional monitoring methods, this paper establishes an open-pit mine monitoring data set, and proposes a real-time intelligent monitoring model based on UAV. The reasoning component with strong computing power and low power consumption is selected, and the lightweight object detection model is selected for the experiment. A quantitative standard of dynamic energy consumption detection by evaluation algorithm is proposed. Through experimental comparison, it is found that YOLOv4-tiny has the highest comprehensive grade in detection accuracy, speed, energy consumption and other aspects, which is suitable for application in the above model.

关 键 词：Open-Pit Mine Safety Monitoring UAV Object Detection 

分 类 号：TD8[矿业工程—矿山开采]