Computer Science ›› 2020, Vol. 47 ›› Issue (10): 187-193.doi: 10.11896/jsjkx.191000035

• Computer Graphics & Multimedia • Previous Articles     Next Articles

Digital Instrument Identification Method Based on Deformable Convolutional Neural Network

GUO Lan-ying, HAN Rui-zhi, CHENG Xin   

  1. School of Information Engineering,Chang’an University,Xi’an 710064,China
  • Received:2019-10-09 Revised:2020-03-12 Online:2020-10-15 Published:2020-10-16
  • About author:GUO Lan-ying,born in 1963,professor.Her main research interests include intelligent transportation system and so on.
    HAN Rui-zhi,born in 1995,postgra-duate,is a member of China Computer Federation.His main research interests include deep learning and computer vision.
  • Supported by:
    Shaanxi Provincial Key Research and Development Program(2019NY-163),Shaanxi Provincial Transportation Science and Technology Project(14-23K) and Central University Basic Research Business Expenses Special Fund Project (300102329101,310824175004)

Abstract: At present,traditional image processing methods and machine learning methods are adopted for the identification of digital display instruments,which have disadvantages such as low recognition accuracy for both characters and numbers in complicated scenarios,and difficulty to meet real-time application requirements.Aiming at the problems above,combining traditional image processing technology and deep learning methods,a method of segmentation and recognition of digital display instrument based on deformable convolutional neural network is proposed.This method includes steps such as image preprocessing,character segmentation and image recognition.Firstly,the GrayWorld algorithm is applied to perform brightness equalization on the image to be recognized for the further using of color segmentation to extract the screen area.Secondly,the projected histogram method is implemented to realize the unified segmentation of characters with its corresponding decimal point after performing morphological operation on the image.Finally,a deformable convolutional neural network is proposed and trained for character recognition,which optimizes the endogenous geometry restriction of receptive field in convolutional neural networks.The experimental results indicate that the addition of deformable convolution effectively improves the accuracy of image recognition and the convergence speed of the network,and the accuracy of the overall recognition method reaches 99.45% and the detection speed is 10FPS,which can meet the requirements of practical applications.

Key words: Image processing, Character recognition, Deformable convolutional neural network, Projection histogram

CLC Number: 

  • TP391.4
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