Image Recognition Technology Based on Deep Learning

  • Fuchao Cheng
  • Hong Zhang
  • Wenjie Fan
  • Barry Harris


In order to apply deep learning to the field of image recognition, the basic principle, training process and model structure of deep belief networks (DBNs) in deep learning are analysed. For small samples, samples are down-sampled at the pretraining stage. In the parameter fine-tuning stage, random dropout is introduced, and the hidden layer nodes are cleared out randomly to keep the weights unchanged. The results show that the layered training mechanism of DBNs greatly reduces the difficulty of training and the training time. In the small sample, after introducing the down-sampling and random dropout, the deep belief network has a good improvement in recognition rate and time consumption, and the over fitting phenomenon is effectively alleviated.


Deep learning Image recognition Deep belief network Small sample set 



This work was supported by the Open Research Subject of Key Laboratory of Internet Intelligent Information Processing of Sichuan Province under Grant No. szjj2017-008.


  1. 1.
    Bai, J., Wu, Y., Zhang, J., & Chen, F. (2015). Subset based deep learning for RGB-D object recognition. Neurocomputing, 165(C), 280–292.CrossRefGoogle Scholar
  2. 2.
    Tan, W., Zhao, C., Wu, H., & Gao, R. (2015). A deep learning network for recognizing fruit pathologic images based on flexible momentum. Nongye Jixie Xuebao/Transactions of the Chinese Society of Agricultural Machinery, 46(1), 20–25.Google Scholar
  3. 3.
    Xing, W., Zhao, S., & Zhang, S. (2017). Blind identification technology of computer generated image based on texture recognition. Journal of Computational and Theoretical Nanoscience, 14(7), 3312–3322.CrossRefGoogle Scholar
  4. 4.
    Su, Z., Qiang, F., Jin, D., Zhan, J., & Jiang, H. (2015). Low contrast target polarization recognition technology based on lifting wavelet. Acta Optica Sinica, 35(2), 124–133.Google Scholar
  5. 5.
    Zaninelli, M., Redaelli, V., Luzi, F., Bontempo, V., Dell’Orto, V., & Savoini, G. (2017). A monitoring system for laying hens that uses a detection sensor based on infrared technology and image pattern recognition. Sensors, 17(6), 1–17.CrossRefGoogle Scholar
  6. 6.
    Guo, H., Zhou, W., & Zhao, Y. (2015). A license plate recognition algorithm based on image processing technology. Metallurgical & Mining Industry, 1(9), 1864–1869.Google Scholar
  7. 7.
    Zhang, R., & Xin, B. (2016). A review of woven fabric pattern recognition based on image processing technology. Research Journal of Textile & Apparel, 20(1), 37–47.MathSciNetCrossRefGoogle Scholar
  8. 8.
    Lv, H., & Jiang, C. (2015). Prison identification technology research based on iris image recognition. International Journal of Security & Its Applications, 9(9), 1–8.CrossRefGoogle Scholar
  9. 9.
    Gongming, W., Junfei, Q., Xiaoli, L., et al. (2017). Improved classification with semi-supervised deep belief network. IFAC-PapersOnLine, 50(1), 4174–4179.CrossRefGoogle Scholar
  10. 10.
    Nagato, T., Shibuya, H., Okamoto, H., & Koezuka, T. (2017). Machine learning technology applied to production lines: Image recognition system. Fujitsu Scientific & Technical Journal, 53(4), 52–58.Google Scholar
  11. 11.
    Han, X. (2015). Analysis on e-ticket image recognition technology based on mobile QR code. Electronics Science Technology and Application, 2(1), 19.CrossRefGoogle Scholar
  12. 12.
    Feng, Q., Liu, D., Sun, B., Liu, R., Ma, Z., & Wang, H. (2016). Identification of alfalfa leaf diseases using image recognition technology. PLoS ONE, 11(12), e0168274.CrossRefGoogle Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.College of Information Science and EngineeringChengdu UniversityChengduChina
  2. 2.Department of Computer ScienceFlorida State UniversityTallahasseeUSA

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