计算机与现代化 ›› 2024, Vol. 0 ›› Issue (12): 100-107.doi: 10.3969/j.issn.1006-2475.2024.12.015

• 信息系统 • 上一篇    下一篇

基于毫米波雷达与视觉融合的电力现场安全帽佩戴检测

 
  

  1. (国网四川省电力公司眉山供电公司,四川 眉山 620010)
  • 出版日期:2024-12-31 发布日期:2024-12-31
  • 基金资助:
    国网四川省电力公司科技项目(521917230001)

Helmet Wearing Detection in Electric Power Field Based on#br# Millimeter-wave Radar and Visual Fusion

  1. (Meishan Power Supply Company of State Grid Sichuan Electric Power Company, Meishan 620010, China)
  • Online:2024-12-31 Published:2024-12-31

摘要: 针对目前纯视觉方案的安全帽检测算法难以兼顾精度与实时性的问题,本文利用毫米波雷达与视觉融合技术,提出一种基于嵌入式设备实时检测电力作业人员是否佩戴安全帽的智能识别算法。首先,对毫米波雷达和相机数据进行空间和时间融合;其次,基于目标生命周期方法对雷达数据进行有效目标筛选,设计状态滤波粗检测作业人员,并计算出图像感兴趣区域(ROI)映射到图像;然后,对多ROI的情况设计去重与合并方法;最后,基于YOLOv5模型,采用改进的轻量级网络ShuffleNetv2作为骨干网络,提高网络运行速度,在ROI中对人员是否佩戴安全帽进行检测。在电力现场搭建实验平台并将该方案与现有的纯视觉方案进行对比实验,结果表明该方法在检测精度有所提高的同时,实时性大幅提升,可以在作业现场实现基于嵌入式设备的实时检测。

关键词: 目标检测, 毫米波雷达, 神经网络, 数据融合, 施工安全

Abstract:  The failure of power site operators to wear safety helmets is one of the important causes of safety accidents. In order to prevent the recurrence of similar accidents, using millimeter-wave radar and vision fusion technology, a set of intelligent recognition algorithms that can detect whether workers are wearing safety helmets in real time based on edge computing equipment has been developed. Firstly, the conversion relationship between the coordinate systems is calculated and the joint calibration is carried out to achieve spatial fusion, Secondly, synchronization of radar and visual data is realized by timestamp matching. Then, the millimeter-wave radar data is preprocessed and the image region of interest (ROI) is calculated; finally, based on the YOLO v5 model, the improved lightweight network ShuffleNetv2 is used as the backbone network and the loss function is replaced to improve the network operation speed, and the personnel wearing safety helmets are detected in the ROI. The experimental platform was built in the electric power field and the algorithm was compared with the existing pure vision scheme. The results showed that the proposed method was slightly improved compared with the existing advanced methods in terms of detection accuracy, and greatly improved compared with the pure vision scheme in terms of real-time performance, which could realize real-time detection at the operation site.

Key words: target detection, millimeter wave radar, neural network, data fusion, construction safety

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