Novel Deep Learning Algorithm for Monocular Vision:H_SFPN
Computer Science

Computer Science ›› 2021, Vol. 48 ›› Issue (4): 130-137.doi: 10.11896/jsjkx.200400090

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Novel Deep Learning Algorithm for Monocular Vision:H_SFPN

SHI Xian-rang1, SONG Ting-lun1,2, TANG De-zhi2, DAI Zhen-yong1   

  1. 1 College of Energy and Power Engineering,Nanjing University of Aeronautics and Astronautics,Nanjing 210001,China
    2 Chery Advanced Engineering & Technology Center,Wuhu,Anhui 241006,China
  • Received:2020-06-24 Revised:2020-07-29 Online:2021-04-15 Published:2021-04-09
  • About author:SHI Xian-rang,born in 1996,postgra-duate.His main research interests include autonomous driving,object detection and pattern recognition.(nuaasxr@163.com)
    SONG Ting-lun,born in 1965,Ph.D,professor,Ph.D supervisor.His main research interests include simulation driven vehicle architecture design and development,autonomous driving vehicles,and data driven energy management strategies for new energy vehicles.
  • Supported by:
    Anhui Provincial Development and Reform Commission’s Major R&D Project.

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Abstract: This paper proposes a single-stage deep learning based H_SFPN algorithm for monocular visual object detection.Compared with the existing YOLOv3 and CenterNet algorithms,the proposed algorithm can effectively improve the accuracy of small object detection without sacrificing the real-time performance.This paper designs a new network architecture (backbone),which uses an improved Hourglass network model to extract feature maps in order to make full use of the high resolution of the underlying features and the high semantic information of the high-level features.Then in the feature map fusion stage,a method SFPN based on the weighted fusion of feature maps is proposed.Finally,the proposed H_SFPN algorithm improves the loss function of the object position and size,which can effectively reduce the training error and accelerate the convergence speed.According to the experimental results on the MSCOCO data set,the proposed H_SFPN algorithm is significantly better than the existing mainstream deep learning object detection algorithms such as Faster-RCNN,YOLOv3 and EfficientDet.Among them,the small object detection index APs of this algorithm is the highest,reaching 32.7.

Key words: Backbone, Deep convolutional neural network, Loss function, Object detection, Weighted fusion

CLC Number: 

  • TP391.41
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