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Research Method of Blind Path Recognition Based on DCGAN

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Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020 (AISI 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1261))

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Abstract

In order to solve the problem that there are few blind path data sets and a lot of manual data collection work in the current blind guide system, computer vision algorithm is used to automatically generate blind path images in different environments. Methods a blind path image generation method based on the depth convolution generative adversary network (DCGAN) is proposed. The method uses the characteristics of typical blind path, which is the combination of depression and bulge. The aim of long short memory network’ (LSTM) is to encode the depression part, and the aim of convolution neural network (CNN) is to encode the bulge part. The two aspects of information are combined to generate blind path images in different environments. It can effectively improve the blind path recognition rate of the instrument and improve the safe travel of the visually impaired. Conclusion generative adversarial networks (GANs) can be used to generate realistic blind image, which has certain application value in expanding blind channel recognition data, but it still needs to be improved in some details.

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Author information

Authors and Affiliations

  1. School of Information Science and Engineering, Fujian University of Technology, Fuzhou, China

    Ling Luo, Ping-Jun Zhang, Peng-Jun Hu, Liu Yang & Kuo-Chi Chang

  2. Fujian Provincial Key Laboratory of Big Data Mining and Applications, Fujian University of Technology, Fuzhou, China

    Kuo-Chi Chang

  3. College of Mechanical and Electrical Engineering, National Taipei University of Technology, Taipei, Taiwan

    Kuo-Chi Chang

  4. Department of Business Administration, North Borneo University College, Sabah, Malaysia

    Kuo-Chi Chang

Authors
  1. Ling Luo
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  2. Ping-Jun Zhang
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  3. Peng-Jun Hu
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  4. Liu Yang
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  5. Kuo-Chi Chang
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Corresponding author

Correspondence to Kuo-Chi Chang .

Editor information

Editors and Affiliations

  1. Faculty of Computers and Artificial Intelligence, Information Technology Department, and Chair of the Scientific Research Group in Egypt, Cairo University, Cairo, Egypt

    Aboul Ella Hassanien

  2. Department of Electronics and Computer Science, Koszalin University of Technology, Koszalin, Poland

    Adam Slowik

  3. Faculty of Electrical Engineering and Computer Science, VĹ B-Technical University of Ostrava, Ostrava-Poruba, Moravskoslezsky, Czech Republic

    Václav Snášel

  4. Rector of the Electronic Research Institute, Cairo, Egypt

    Hisham El-Deeb

  5. Faculty of computers and information, Ain Shams University, Cairo, Egypt

    Fahmy M. Tolba

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© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

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Luo, L., Zhang, PJ., Hu, PJ., Yang, L., Chang, KC. (2021). Research Method of Blind Path Recognition Based on DCGAN. In: Hassanien, A.E., Slowik, A., Snášel, V., El-Deeb, H., Tolba, F.M. (eds) Proceedings of the International Conference on Advanced Intelligent Systems and Informatics 2020. AISI 2020. Advances in Intelligent Systems and Computing, vol 1261. Springer, Cham. https://doi.org/10.1007/978-3-030-58669-0_8

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