计算机科学 ›› 2022, Vol. 49 ›› Issue (6A): 424-428.doi: 10.11896/jsjkx.220100252

• 图像处理&多媒体技术 • 上一篇    下一篇

基于外接圆半径差损失的实时安全帽检测算法

陈永平1, 朱建清1,2, 谢懿1, 吴含笑3, 曾焕强1   

  1. 1 华侨大学工学院 福建 泉州 362021
    2 华侨大学信息科学与工程学院 福建 厦门 361021
    3 厦门亿联网络技术股份有限公司 福建 厦门 361015
  • 出版日期:2022-06-10 发布日期:2022-06-08
  • 通讯作者: 朱建清(jqzhu@hqu.edu.cn)
  • 作者简介:(956086636@qq.com)
  • 基金资助:
    国家自然科学基金面上项目(61976098,61871434);国家重点研发计划项目(2021YFE0205400);福建省自然科学基金杰出青年项目(2019J06017);福厦泉国家自主创新示范区协同创新平台项目(2021FX03)

Real-time Helmet Detection Algorithm Based on Circumcircle Radius Difference Loss

CHEN Yong-ping1, ZHU Jian-qing1,2, XIE Yi1, WU Han-xiao3, ZENG Huan-qiang1   

  1. 1 College of Engineering,Huaqiao University,Quanzhou,Fujian 362021,China
    2 College of Information Science and Engineering,Huaqiao University,Xiamen,Fujian 361021,China
    3 Xiamen Yealink Network Technology Co.,LTD,Xiamen,Fujian 361015,China
  • Online:2022-06-10 Published:2022-06-08
  • About author:CHEN Yong-ping,born in 1998,postgraduate.His main research interests include image processing and deep learning.
    ZHU Jian-qing,born in 1987,professor.His main research interests include deep learning,machine vision and pattern recognition.
  • Supported by:
    National Natural Science Foundation of China(61976098,61871434),National Key R & D Program of China(2021YFE0205400),Natural Science Foundation for Outstanding Young Scholars of Fujian Province(2019J06017) and Collaborative Innovation Platform Project of Fuxiaquan National Independent Innovation Demonstration Zone(2021FX03).

摘要: 针对安全帽检测算法的快速且精准需求,提出了一种实时安全帽检测算法。首先,针对基于边界框回归损失函数容易出现梯度消失(Gradient Vanish)的问题,本文提出外接圆半径差(Circumcircle Radius Difference,CRD)损失函数;然后,针对复杂多尺度特征融合层制约检测速度的问题,提出了一种轻量化的小目标聚焦型(Focus on Small Object,FSO)特征融合层;最后本文结合YOLO网络、CRD和FSO形成YOLO-CRD-FSO(YCF)检测模型,实现实时安全帽检测。实验结果表明,在Jetson Xavier NX设备上检测分辨率为640×640的视频,YCF的检测速度达到43.4帧/秒,比当前最新锐的YOLO-V5模型的速度快了近2帧/秒,且均值平均精度提升了近1%。说明YCF检测模型综合优化了边界框回归损失函数和特征融合层,获得了良好的安全帽检测效果。

关键词: YOLO, 安全帽检测, 边界框回归, 目标检测, 特征融合层

Abstract: For large demands of fast and accurate helmet detection,this paper proposes a real-time helmet detection algorithm.Firstly,to solve the gradient vanish problem of using bounding box regression loss functions,this paper proposes the circumcircle radius difference (CRD) loss function.Secondly,to solve the problem of complex multi-scale feature fusion layers restricting detection speeds,this paper proposes a lightweight focus on small object (FSO) feature fusion layer.Finally,this paper combines the YOLO network,CRD,and FSO to form a YOLO-CRD-FSO (YCF) model for real-time helmet detection.On a Jetson Xavier NX device,experiments show that the detection speed of YCF reaches 43.4 frames per second for 640×640 sized videos,which is nearly 2 frames per second faster than the state-of-the-art YOLO-V5 model,and the mean average precision has been improved by nearly 1%.The proposed YCF detection model comprehensively optimizes boundary box regression loss functions and feature fusions,acquiring good helmet detection results.

Key words: Bounding box regression, Feature fusion layer, Object detection, Safety helmet detection, YOLO

中图分类号: 

  • TP391
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