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
[1]CUI W X,CUI Y C,WANG Z H,et al.License plate character segmentation algorithm based on template matching and vertical projection[J].Journal of Qiqihar University(Natural Science Edition),2015,31(6):12-16.
[2]LOWE D G.Distinctive image features from scale-invariant keypoints[J].International Journal of Computer Vision,2004,60(2):91-110.
[3]DALAL N,TRIGGS B.Histograms of oriented gradients forhuman detection[C]//IEEE Computer Society Conference on Computer Vision and Pattern Recognition(CVPR).San Diego,USA:IEEE,2002,1:886-893.
[4]DENG J,DONG W,SOCHER R,et al.ImageNet:A Large-Scale Hierarchical Image Database[C]//IEEE Computer Vision and Pattern Recognition (CVPR).2009.
[5]RUSSAKOVSKY O,DENG J,et al.ImageNet Large Scale Visual Recognition Challenge[J].International Journal of Computer Vision(IJCV),2015,115:211-252.
[6]LECUN Y,BOTTOU L,BENGIO Y,et al.Gradient-basedlearning applied to document recognition[J].Proceedings of the IEEE,1998,86(11),2278-2324.
[7]LECUN Y,CORTES C.MNIST handwritten digit database[EB/OL].http://yann.lecun.com/exdb/mnist,2010.
[8]KRIZHEVSKY A,SUTSKEVER I,HINTON G E.Imagenetclassification with deep convolution neural network[C]//Advances in Neural Information Processing System.Cambridge:MIT Press,2012:1097-1105.
[9]LECUN Y,BENGIO Y.The Handbook of Brain Theory and Neural Networks[M].Cambridge:MIT Press,1998:255-258.
[10]XIE S,GIRSHICK R,DOLLAR P,et al.Aggregated ResidualTransformations for Deep Neural Networks[C]//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).IEEE Computer Society,2017.
[11]LIN T Y,GOYAL P,GIRSHICK R,et al.Focal loss for dense object detection[C]//ICCV.2017.
[12]DAI J,LI Y,HE K,et al.R-fcn:Object detection via region-based fully convolutional networks[C]//NeurIPS.2016.
[13]GIRSHICK R,DONAHUE J,DARRELL T,et al.Rich feature hierarchies for accurate object detection and semantic segmentation[C]//CVPR.2014.
[14]HE K,GKIOXARI G,DOLL′AR P,et al.Mask r-cnn[C]//ICCV.2017.
[15]SZEGEDY C,LIU W,JIA Y,et al.Going deeper with convolutions[C]//IEEE Conference on Computer Vision and Pattern Recognition(CVPR).Boston,USA,2015:1-9.
[16]LIN M,CHEN Q,YAN S.Network in network[EB/OL].http://arxiv.org/abs/1312.4400,2013.
[17]SIMONYAN K,ZISSERMAN A.Very deep convolutional networks for large-scale image recognition[J].arXiv:1409.1556,2014.
[18]HE K,ZHANG X,REN S,et al.Deep residual learning for image recognition[C]//CVPR.2016.
[19]BOUREAU Y L,PONCE J,LECUN Y.A theoretical analysis of feature pooling in visual recognition[C]//ICML.2010.
[20]FELZENSZWALB P F,GIRSHICK R B,MCALLESTER D,et al.Object detection with discriminatively trained part based models[C]//TPAMI.2010.
[21]JADERBERG M,SIMONYAN K,ZISSERMAN A,et al.Spatial transformer networks[C]//NIPS.2015.
[22]LUO W,LI Y,URTASUN R,et al.Understanding the effective receptive field in deep convolutional neural networks[J].arXiv:1701.04128,2017.
[23]YU F,KOLTUN V.Multi-scale context aggregation by dilated convolutions[C]//ICLR.2016.
[24]DAI J F,QI H Z,XIONG Y W.Deformable convolutional networks[C]//Proceedings of the IEEE International Conference on Computer Vision.Venice,Italy:IEEE,2017:764-773.
[25]WANG F,XIANG D.Digital instrument identification methodbased on convolutional neural network[J].Machine Design and Manufacturing Engeering,2018,9(47):63-66.
[1] SONG Ya-fei, CHEN Yu-zhang, SHEN Jun-feng and ZENG Zhang-fan. Underwater Image Reconstruction Based on Improved Residual Network [J]. Computer Science, 2020, 47(6A): 500-504.
[2] CAI Yu-xin, TANG Zhi-wei, ZHAO Bo, YANG Ming and WU Yu-fei. Accelerated Software System Based on Embedded Multicore DSP [J]. Computer Science, 2020, 47(6A): 622-625.
[3] MA Hong. Fusion Localization Algorithm of Visual Aided BDS Mobile Robot Based on 5G [J]. Computer Science, 2020, 47(6A): 631-633.
[4] MIAO Yi, ZHAO Zeng-shun, YANG Yu-lu, XU Ning, YANG Hao-ran, SUN Qian. Survey of Image Captioning Methods [J]. Computer Science, 2020, 47(12): 149-160.
[5] LING Chen, ZHANG Xin-tong, MA Lei. Remote Sensing Image Processing Technology and Its Application Based on Mask R-CNN Algorithms [J]. Computer Science, 2020, 47(10): 151-160.
[6] ZHU De-li, YANG De-gang, HU Rong, WAN Hui. Adaptive Multi-level Threshold Binaryzation Method for Optical Character Recognition in Mobile Environment [J]. Computer Science, 2019, 46(8): 315-320.
[7] PAN Wei-qiong, TU Juan-juan, GAN Zong-liang, LIU Feng. Low Light Images Enhancement Based on Retinex Adaptive Reflectance Estimation and LIPS Post-processing [J]. Computer Science, 2019, 46(8): 327-331.
[8] MA Li-xin, LI Feng-kun. Light-weight Recognition Algorithm of Vehicle License Plate Characters [J]. Computer Science, 2019, 46(6A): 239-241.
[9] HAN Ke-kun, HU Gui-chuan, REN Jing, HE Hong-yu, LIU Jia-yin. Application of Image Processing in Feature Size Detection of Wind Turbine Blade’s Flange Face [J]. Computer Science, 2019, 46(6A): 562-565.
[10] YANG Xiu-zhang, XIA Huan, YU Xiao-min. Image Enhancement and Recognition Method Based on Shui-characters [J]. Computer Science, 2019, 46(11A): 324-328.
[11] GU Wei-dong, LI Bing. Automatic Color Image Segmentation Algorithm Based on Random Region Merging [J]. Computer Science, 2018, 45(9): 279-282.
[12] ZHANG Wen-yong, CHEN Le-zhu. Tableware Sorting System Based on LabVIEW Machine Vision [J]. Computer Science, 2018, 45(6A): 595-597.
[13] LIN Wei-jun, ZHAO Liao-ying, LI Xiao-run. Real-time Sub-pixel Object Detection for Hyperspectral Image Based on Pixel-by-pixel Processing [J]. Computer Science, 2018, 45(6): 259-264.
[14] ZHONG Ping-chuan, WANG Na, XIAO Yi-di, ZHENG Ze-zhong. Research on High Rate of Log’s Output Based on Computer Vision [J]. Computer Science, 2018, 45(11A): 176-179.
[15] ZHANG Xiu-feng, WANG Juan, DING Qiang. Research on Intelligent Detection Method of Steel Rail Abrasion [J]. Computer Science, 2018, 45(11A): 274-277.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed   
[1] ZHANG Jia-nan and XIAO Ming-yu. Approximation Algorithm for Weighted Mixed Domination Problem[J]. Computer Science, 2018, 45(4): 83 -88 .
[2] WU Jian-hui, HUANG Zhong-xiang, LI Wu, WU Jian-hui, PENG Xin and ZHANG Sheng. Robustness Optimization of Sequence Decision in Urban Road Construction[J]. Computer Science, 2018, 45(4): 89 -93 .
[3] ZHOU Yan-ping and YE Qiao-lin. L1-norm Distance Based Least Squares Twin Support Vector Machine[J]. Computer Science, 2018, 45(4): 100 -105 .
[4] LIU Bo-yi, TANG Xiang-yan and CHENG Jie-ren. Recognition Method for Corn Borer Based on Templates Matching in Muliple Growth Periods[J]. Computer Science, 2018, 45(4): 106 -111 .
[5] YANG Yu-qi, ZHANG Guo-an and JIN Xi-long. Dual-cluster-head Routing Protocol Based on Vehicle Density in VANETs[J]. Computer Science, 2018, 45(4): 126 -130 .
[6] HAN Kui-kui, XIE Zai-peng and LV Xin. Fog Computing Task Scheduling Strategy Based on Improved Genetic Algorithm[J]. Computer Science, 2018, 45(4): 137 -142 .
[7] ZHENG Xiu-lin, SONG Hai-yan and FU Yi-peng. Distinguishing Attack of MORUS-1280-128[J]. Computer Science, 2018, 45(4): 152 -156 .
[8] LIU Qin. Study on Data Quality Based on Constraint in Computer Forensics[J]. Computer Science, 2018, 45(4): 169 -172 .
[9] RAN Zheng, LUO Lei, YAN Hua and LI Yun. Study on Automatic Method for AUTOSAR Runnable Entity-task Mapping[J]. Computer Science, 2018, 45(4): 190 -195 .
[10] GUO Jun-xia, GUO Ren-fei, XU Nan-shan and ZHAO Rui-lian. Study on Construction of EFSM Model for Web Application Based on Session[J]. Computer Science, 2018, 45(4): 203 -207 .