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基于香橙派的矿井电梯安全检测系统的研究与设计
Research and Design of Mine Elevator Safety Detection System Based on Orange Pie

DOI: 10.12677/me.2024.124093, PP. 793-805

Keywords: 香橙派,STM32,深度学习,目标检测与跟踪,改进YOLOv4,矿井安全
Orange Pi
, STM32, Deep Learning, Object Detection and Tracking, Improved YOLOv4, Mine Safety

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Abstract:

矿井电梯作为矿业施工现场不可或缺的垂直运输设备,其运行的安全性直接关系到矿工的生命安全。因此,开发一种高效、准确的矿井电梯安全检测系统显得尤为重要。基于以上要求,提出设计并实现了一套混合检测控制系统,将香橙派和STM32F1系列处理器结合,利用深度学习进行实时目标检测与跟踪,并且使用改进YOLOv4进行目标的安全帽检测,以提升矿工进入电梯时的安全帽佩戴率,保证矿井安全。
As an indispensable vertical transportation equipment in mining construction sites, the safety of mine elevators is directly related to the life safety of miners. Therefore, developing an efficient and accurate safety detection system for mine elevators is particularly important. Based on the above requirements, a hybrid detection and control system was proposed and implemented, which combines Orange Pie and STM32F1 series processors, uses deep learning for real-time object detection and tracking, and uses improved YOLOv4 for safety helmet detection of targets to improve the safety helmet wearing rate of miners entering elevators and ensure mine safety.

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