Journal of Computer Applications ›› 2022, Vol. 42 ›› Issue (1): 273-279.DOI: 10.11772/j.issn.1001-9081.2021020333

• Frontier and comprehensive applications • Previous Articles    

Valve identification method based on double detection

Wei SHE1,2, Qian ZHENG2, Zhao TIAN1, Wei LIU1,2, Yinghao LI1,2()   

  1. 1.Collage of Software,Zhengzhou University,Zhengzhou Henan 450002,China
    2.Henan Collaborative Innovation Center of Internet Medical and Health Services (Zhengzhou University),Zhengzhou Henan 450052,China
  • Received:2021-03-05 Revised:2021-04-16 Accepted:2021-04-22 Online:2021-04-27 Published:2022-01-10
  • Contact: Yinghao LI
  • About author:SHE Wei, born in 1977, Ph. D., professor. His research interests include blockchain, information security, trusted distributed system.
    ZHENG Qian, born in 1996, M. S. candidate. Her research interests include image recognition, machine learning.
    TIAN Zhao, born in 1985, Ph. D., lecturer. His research interests include artificial intelligence, information security.
    LIU Wei, born in 1981, Ph. D., associate professor. His research interests include blockchain, wireless network.
    LI Yinghao, born in 1987, Ph. D., lecturer. His research interests include digital image processing, pattern recognition, machine learning.
  • Supported by:
    National Key Research and Development Program(2020YFB1712401);Science and Technology Project of Henan Province(212102310039);Science and Technology Research and Development Plan of China Railway Beijing Group Company Limited(2021AY03)


佘维1,2, 郑倩2, 田钊1, 刘炜1,2, 李英豪1,2()   

  1. 1.郑州大学 软件学院,郑州 450002
    2.互联网医疗与健康服务河南省协同创新中心(郑州大学),郑州 450052
  • 通讯作者: 李英豪
  • 作者简介:佘维(1977—),男,湖南常德人,教授,博士,CCF会员,主要研究方向:区块链、信息安全、可信分布式系统
  • 基金资助:


Aiming at the problems that current valve identification methods in industry have high missed rate of overlapping targets, low detection precision, poor target encapsulation degree and inaccurate positioning of circle center, a valve identification method based on double detection was proposed. Firstly, data enhancement was used to expand the samples in a lightweight way. Then, Spatial Pyramid Pooling (SPP) and Path Aggregation Network (PAN) were added on the basis of deep convolutional network. At the same time, the anchor boxes were adjusted and the loss function was improved to extract the valve prediction boxes. Finally, the Circle Hough Transform (CHT) method was used to secondarily identify the valves in the prediction boxes to accurately identify the valve regions. The proposed method was compared with the original You Only Look Once (YOLO)v3, YOLOv4, and the traditional CHT methods, and the detection results were evaluated by jointly using precision, recall and coincidence degree. Experimental results show that the average precision and recall of the proposed method reaches 97.1% and 94.4% respectively, 2.9 percentage points and 1.8 percentage points higher than those of the original YOLOv3 method respectively. In addition, the proposed method improves the target encapsulation degree and location accuracy of target center. The proposed method has the Intersection Over Union (IOU) between the corrected frame and the real frame reached 0.95, which is 0.05 higher than that of the traditional CHT method. The proposed method improves the success rate of target capture while improving the accuracy of model identification, and has certain practical value in practical applications.

Key words: target detection, valve identification, You Only Look Once (YOLO) method, Circle Hough Transform (CHT), secondary identification



关键词: 目标检测, 气门识别, YOLO方法, 霍夫圆变换, 二次识别

CLC Number: