Abstract
In the engine ignition experiment, the specific instant of the ignition is usually obtained from a large quantity of high-resolution pictures taken with high-speed cameras, which puts forward an urgent request for the rapid image recognition. To address this issue, a picture recognition method based on convolutional neural network (CNN) is described. First, a training data set for the CNN model is made based on the original experimental images. Second, the constructed CNN model is trained to obtain the classification result. Finally, the CNN model is evaluated and optimized for the image recognition of engine ignition. The experimental results show that the method can quickly and accurately recognize the engine ignition.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Z. Kaifei, Applications of artificial intelligence and its future prospects. J. Lvliang High. College 26(4), 79–81 (2010)
R.K. Lindsay, E.A. Feigenbaum, B.G. Buchanan et al., Applications of artificial intelligence for chemical inference: the dendral project. Electr. Power Syst. Res. 41(96), 117–131 (1980)
B. Fuqing, Image recognition and classification based on artificial neural network (Chengdu University of Technology, 2010)
W. Yongqing, Principle and method of artificial intelligence (Xi’an Jiao Tong University press, 1998)
Y. Wei, X. Wei, M. Lin et al., HCP: a flexible cnn framework for multi-label image classification. IEEE Trans. Pattern Anal. Mach. Intell. 38(9), 1901–1907 (2016)
D. Silver, A. Huang, C.J. Maddison et al., Mastering the game of go with deep neural networks and tree search. Nature 529(7587), 484 (2016)
D. Silver, J. Schrittwieser, K. Simonyan et al., Mastering the game of go without human knowledge. Nature 550(7676), 354–359 (2017)
Hu Kui, Zhang Dongping, Yang Li, Multi-scale pedestrian detection based on convolutional neural networks. J. China Univ. Metrol. 4, 472–477 (2017)
B. Chenjia, Research on autonomous driving method based on computer vision and deep learning (Harbin Institute of Technology, 2017)
S. Guibin, Research on road scene object detection based on depth convolutional neural network (Northern Polytechnical University, 2017)
S. Ai, L. Jia, C. Zhuang et al., A registration method for 3D point clouds with convolutional neural network (2017)
A. Krizhevsky, I. Sutskever, G.E. Hinton, ImageNet classification with deep convolutional neural networks, in International Conference on Neural Information Processing Systems (Curran Associates Inc. 2012), pp. 1097–1105
X. Ke, Study of convolutional neural network application on image recognition (Zhejiang University, 2012)
W. Zhen, G. Maoting, Design and implementation of image recognition algorithm based on convolution neural network, in Modern Computer, vol. 7 (Professional Edition, 2015), pp. 61–66
J. Fu, H. Zheng, T. Mei, Look closer to see better: recurrent attention convolutional neural network for fine-grained image recognition, in IEEE Conference on Computer Vision and Pattern Recognition (IEEE Computer Society, 2017), pp. 4476–4484
J. Bai, Z. Chen, B. Feng et al., Image character recognition using deep convolutional neural network learned from different languages, in IEEE International Conference on Image Processing (IEEE, 2015), pp. 2560–2564
G.E. Hinton, R.R. Salakhutdinov, Reducing the dimensionality of data with neural networks. Science 313(5786), 504–507 (2006)
Y. Lecun, B. Boser, J.S. Denker et al., Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541–551 (2014)
Y. Lecun, L. Bottou, Y. Bengio et al., Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)
T.C. Wang, J.Y. Zhu, E. Hiroaki et al., A 4D light-field dataset and CNN architectures for material recognition, in Computer Vision—ECCV 2016 (Springer International Publishing, 2016), pp. 121–138
Z. Xiaohua, S.S. Guang, C. Bo et al., CAS2PEAL: a large2scale Chinese face database and some primary evaluations. J Comput-Aid Design Comput. Graph. 17(1), 9–17 (2005)
J. Guo, S. Gould, Deep CNN ensemble with data augmentation for object detection (Computer Science, 2015)
Acknowledgements
This work was supported by the National Key Research and Development 370 Plan of China under Grant No. 2017YFB0202101. We also express our gratitude to Lanying Ge (ROMTEC) for his technical supports.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Huang, S., Lu, F., Pang, Y., Xiao, S. (2019). Image Recognition of Engine Ignition Experiment Based on Convolutional Neural Network. In: Jia, Y., Du, J., Zhang, W. (eds) Proceedings of 2018 Chinese Intelligent Systems Conference. Lecture Notes in Electrical Engineering, vol 528. Springer, Singapore. https://doi.org/10.1007/978-981-13-2288-4_35
Download citation
DOI: https://doi.org/10.1007/978-981-13-2288-4_35
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-2287-7
Online ISBN: 978-981-13-2288-4
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)