计算机与现代化 ›› 2023, Vol. 0 ›› Issue (10): 59-64.doi: 10.3969/j.issn.1006-2475.2023.10.009

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基于轻量化YOLOv5的安全帽检测

  

  1. (1.国电南瑞科技股份有限公司,江苏 南京 210000; 2.南京信息工程大学计算机学院,江苏 南京 210044)
  • 出版日期:2023-10-26 发布日期:2023-10-26
  • 作者简介:李延满(1974—),男,陕西安康人,高级工程师,硕士,研究方向:配网自动化,配电网智能运维,E-mail: liyanman@sgepri.sgcc.com.cn; 王必恒(1981—),男,贵州务川人,高级工程师,本科,研究方向:电气及其自动化,配网自动化,E-mail: wangbiheng@sgepri.sgcc.com.cn; 通信作者:赵羚焱(2003—),女,江苏江阴人,本科生,研究方向:边缘计算,配电网调度,E-mail: zeo__yan@126.com。
  • 基金资助:
    国家自然科学基金资助项目(61601235); 国电南瑞科技股份有限公司项目(2022h275)

Safety Helmet Detection Based on Lightweight YOLOv5

  1. (1. Nari Technology Co., Ltd., Nanjing 210000, China;
    2. School of Computer Science, Nanjing University of Information Science & Technology, Nanjing 210044, China)
  • Online:2023-10-26 Published:2023-10-26

摘要: 配电网运维施工安全智能监控系统中存在大量数据,客观上要求算法具有较高实时性。基于此,本文轻量化改进YOLOv5算法,具体包括改进K-means算法聚类锚框,采用Hard-swish激活函数和CRD损失函数,同时在主干网融合ShuffleNet结构以及FPN模块增加Attention机制。该模型SNAM-YOLOv5 (ShuffleNet and Attention Mechanism-You Only Look Once version 5)能够显著提高小目标和遮挡目标的检测性能以及处理速度。在基于海思Hi3559A嵌入式平台进行安全帽检测的运行结果表明,该模型优于同类算法,同时具有良好的实时性。

关键词: 关键词:深度学习, 配电网运维, 施工安全, 智能监控, 轻量化网络, 安全帽检测

Abstract: There is a large amount of data in the intelligent monitoring system of distribution network, which objectively requires the algorithm to have high real-time performance. Based on this, the YOLOv5 algorithm is improved in light weight, including improving the K-means algorithm clustering anchor box, using the Hard-swish activation function and the CRD loss function, and at the same time integrating the ShuffleNet structure in the backbone network and adopting the Attention mechanism in the FPN module. The presented model, SNAM-YOLOv5 (ShuffleNet and Attention Mechanism-You Only Look Once version 5), can significantly improve the detection performance and the processing speed of small targets and occluded targets. The results of safety helmet detection based on HiSilicon Hi3559A embedded platform show that the model is superior to similar algorithms and has good real-time performance.

Key words: Key words: deep learning, distribution network operation and maintenance, construction safety, intelligent monitoring, lightweight network, safety helmet detection

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