Skip to main content
SpringerLink
Log in

Improvement of the AlexNet Networks for Large-Scale Recognition Applications

  • Research Paper
  • Published:
Iranian Journal of Science and Technology, Transactions of Electrical Engineering Aims and scope Submit manuscript

Abstract

In this paper, the deep convolutional neural networks (DCNNs) are studied to perform the complex feature extraction on the image in the convolution layer and to improve the final test accuracy of the network. By improving the DCNNs algorithm and framework, it can enhance the accurate extraction of the image features. We replace the fully connection layer of the original network with the global average pooling layer. In the absence of the large number of calculations of network parameters, the final effect is not changed; thereby, it increases the speed of the network. The simulation result is given to show the effectiveness of the DCNNs algorithm by comparing the training accuracy and test accuracy of the five improvement algorithms.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Anbarasu B, Anitha G (2018) Indoor scene recognition for micro aerial vehicles navigation using enhanced-GIST descriptors. Defence Science Journal 68(2):129–137

    Article  Google Scholar 

  • Anwar S, Hwang K, Sung W (2015) Fixed point optimization of deep convolutional neural networks for object recognition. In: Proceedings of the IEEE international conference on acoustics, speech and signal processing, pp 1131–1135

  • Bai C, Huang L, Chen JN, Pan X, Chen SY (2018) Optimization of deep convolutional neural network for large scale image classification. Journal of Software 29(4):1029–1038

    MATH  Google Scholar 

  • Cheng J, Park JH, Zhang L, Zhu Y (2018) An asynchronous operation approach to event-triggered control for fuzzy Markovian jump systems with general switching policies. IEEE Transactions on Fuzzy Systems 26(1):6–18

    Article  Google Scholar 

  • Ciresan DC, Meier U, Masci J, Gambardella LM, Schmidhuber J (2011) Flexible, high performanceconvolutional neural networks for image classification. In: Proceedings of the 22nd international joint conference on artificial intelligence, Barcelona, Catalonia, pp 1237–1242

  • Ge Z, Chang F, Liu H (2017) Multi-target tracking based on Kalman filtering and optical flow histogram. In: Proceedings of the Chinese Automation Congress, Jinan, China. IEEE Press, pp 2540–2545

  • Han SH, Lee KY (2018) Implemetation of image classification CNN using multi thread GPU. In: Proceedings of the 2017 international SoC design conference, pp 296–297

  • He S, Liang A, Lin L, Song T (2017) A continuously adaptive template matching algorithm for human tracking. Robot Comput 303–309

  • Huang K, Lao L (2015) Auto anti-interference sliding mode control based on active front steering. In: Proceedings of The 27th Chinese control and decision conference, Qingdao, China. IEEE Press, pp 3533–3538

  • Ioffe S, Szegedy C (2015) Batch normalization: accelerating deep network training by reducing internal covariate shift. In: Proceedings of the international conference on machine learning, pp 448–456

  • Liu SK, Yan XP, Li P, Hao XH, Wang K (2018) Radar emitter recognition based on SIFT position and scale features. IEEE Transactions on Circuits and Systems II-Express Briefs 65(12):2062–2066

    Article  Google Scholar 

  • McFonnell MD, Vladusich T (2015) Enhanced image classification with a fast-learning shallow convolutional neural network. In: Proceedings of the (2015) international joint conference on neural networks. IEEE Press, Killarney, p 2015

  • Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. In: Proceedings of the international conference on computer vision theory application, pp 331–340

  • Philipp GP, Pimentel J, Motamedi M, Ghiasi S (2018) Ristretto: A framework for empirical study of resource-efficient inference in convolutional neural networks. IEEE Trans Neural Networks Learn Syst 29(11):5784–5789

    Article  Google Scholar 

  • Sharma N, Jain V, Mishra A (2018) An analysis of convolutional neural networks for image classification. Procedia Comput Sci 132:377–384

    Article  Google Scholar 

  • Shen H, Zhu Y, Zhang L, Park JH (2017) Extended dissipative state estimation for markov jump neural networks with unreliable links. IEEE Transactions on Neural Networks and Learning Systems 28(2):346–358

    Article  MathSciNet  Google Scholar 

  • Sudeep KS, Pal KK (2016) Preprocessing for image classification by convolutional neural networks. In: Proceedings of the IEEE international conference on recent trends in electronics, information and communication technology, pp 1778–1781

  • Sun M, Han TX, Liu M C (2016) Multiple instance learning convolutional neural networks for object recognition. In: Proceedings of the ICPR international conference on pattern recognition, pp 3270–3275

  • Valmadre J, Bertinetto L, Henriques J, Vedaldi A, Philip H, Torr S (2017) End-to-end representation learning for correlation filter based tracking. In: Proceedings of the 2017 IEEE conference on computer vision and pattern recognition, pp 5000–5008

  • Wang ZR, Qiao F, Liu Z, Shan YX, Zhou XY, Luo L, Yang HZ (2016) Optimizing convolutional neural network on FPGA under heterogeneous computing framework with OpenCL. In: Proceedings of the IEEE region 10 annual international conference, pp 3433–3438

  • Wang J, Zhu H, Yu S (2017) Object tracking using color-feature guided network generalization and tailored feature fusion. Neurocomputing 238:387–398

    Article  Google Scholar 

  • Yang G, He S, Shi Z (2017) Leveraging crowdsourcing for efficient malicious users detection in large-scale social networks. IEEE Internet Things J 4(2):330–339

    Article  Google Scholar 

  • Zhang Z (1999) Flexible camera calibration by viewing a plane from unknown orientations. In: Proceedings of the 7th IEEE international conference on computer vision, pp 666–673

  • Zhang H, Zheng W (2018) Denial-of-service power dispatch against linear quadratic control via a fading channel. IEEE Trans Autom Control 63(9):3032–3039

    Article  MathSciNet  Google Scholar 

  • Zhang H, Cheng P, Shi L, Chen J (2016) Optimal DoS attack scheduling in wireless networked control system. IEEE Trans Control Syst Technol 24(3):843–852

    Article  Google Scholar 

  • Zhang L, Li K, Zhang Y, Qi Y, Yang L (2017) Adaptive image segmentation based on color clustering for person re-identification. Soft Comput 21(19):5729–5739

    Article  Google Scholar 

  • Zhang L, Zhu Y, Zheng W (2017) State estimation of discrete-time switched neural networks with multiple communication channels. IEEE Transactions on Cybernetics 47(4):1028–1040

    Article  Google Scholar 

  • Zhang H, Qi Y, Wu J, Fu L, He L (2018) DoS attack energy management against remote state estimation. IEEE Transactions on Control of Network Systems 5(1):383–394

    Article  MathSciNet  Google Scholar 

  • Zhong Z, Zhu Y, Lam HK (2018) Asynchronous piecewise output-feedback control for large-scale fuzzy systems via distributed event-triggering schemes. IEEE Transactions on Fuzzy Systems 26(3):1688–1703

    Article  Google Scholar 

  • Zhu Y, Zhang L, Basin MV (2016) Nonstationary H dynamic output feedback control for discrete-time Markov jump linear systems with actuator and sensor saturations. International Journal of Robust and Nonlinear Control 26(5):1010–1025

    Article  MathSciNet  Google Scholar 

  • Zhu Y, Zhong Z, Basin MV, Zhou D (2018) A descriptor system approach to stability and stabilization of discrete-time switched PWA systems. IEEE Transactions on Automatic Control 63(10):3456–3463

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

This work was supported in part by the Foundation for Distinguished Young Scholars of Anhui Province under Grant 1608085J05 and the Key Support Program for University Outstanding Youth Talent of Anhui Province under Grant gxydZD2017001.

Author information

Authors and Affiliations

  1. School of Electrical Engineering and Automation, Anhui University, Hefei, 230601, People’s Republic of China

    Zixian Wu & Shuping He

  2. Institute of Physical Science and Information Technology, Anhui University, Hefei, 230601, People’s Republic of China

    Shuping He

Authors
  1. Zixian Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Shuping He
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Shuping He.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, Z., He, S. Improvement of the AlexNet Networks for Large-Scale Recognition Applications. Iran J Sci Technol Trans Electr Eng 45, 493–503 (2021). https://doi.org/10.1007/s40998-020-00388-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40998-020-00388-4

Keywords

Search

Navigation