Computer Science

Computer Science ›› 2022, Vol. 49 ›› Issue (6A): 424-428.doi: 10.11896/jsjkx.220100252

• Image Processing & Multimedia Technology • Previous Articles     Next Articles

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).

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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

CLC Number: 

  • TP391
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