Journal of Advanced Information Technology and Convergence (한국정보기술학회 영문논문지)

Korean Institute of Information Technology (한국정보기술학회)
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Map Detection using Deep Learning

  • Oh, Byoung-Woo (Dept. of Computer Engineering, Kumoh National Institute of Technology)
  • Received : 2020.12.09
  • Accepted : 2020.12.26
  • Published : 2020.12.31
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Abstract

Recently, researches that are using deep learning technology in various fields are being conducted. The fields include geographic map processing. In this paper, I propose a method to infer where the map area included in the image is. The proposed method generates and learns images including a map, detects map areas from input images, extracts character strings belonging to those map areas, and converts the extracted character strings into coordinates through geocoding to infer the coordinates of the input image. Faster R-CNN was used for learning and map detection. In the experiment, the difference between the center coordinate of the map on the test image and the center coordinate of the detected map is calculated. The median value of the results of the experiment is 0.00158 for longitude and 0.00090 for latitude. In terms of distance, the difference is 141m in the east-west direction and 100m in the north-south direction.

Keywords

Acknowledgement

This research was supported by Kumoh National Institute of Technology (2018-104-082)

References

  1. Y. Gurovich, Y. Hanani, O. Bar, G. Nadav, N. Fleischer, D. Gelbman, L.B. Salmon, P.M. Krawitz, S.B. Kamphausen, M. Zenker, L.M. Bird, and K.W. Gripp, "Identifying facial phenotypes of genetic disorders using deep learning," Nature medicine, Vol. 25, No. 1, pp. 60-64, Jan. 2019. https://doi.org/10.1038/s41591-018-0279-0
  2. B.A.M. Ashqar, B.S. Abu-Nasser, and S.S. Abu-Naser, "Plant Seedlings Classification Using Deep Learning," International Journal of Academic Information Systems Research (IJAISR), Vol. 3, No. 1, pp. 7-14, Jan. 2019.
  3. R. Vinayakumar, M. Alazab, K.P. Soman, P. Poornachandran, and S. Venkatraman, "Robust intelligent malware detection using deep learning," IEEE Access, Vol.7, pp. 46717-46738, Apr. 2019. https://doi.org/10.1109/access.2019.2906934
  4. P. Jiang, Y. Chen, B. Liu, D. He, and C. Liang, "Real-time detection of apple leaf diseases using deep learning approach based on improved convolutional neural networks," IEEE Access, Vol. 7, pp.59069-59080, May 2019. https://doi.org/10.1109/access.2019.2914929
  5. Y. Chen, W. Li, C. Sakaridis, D. Dai, and L.V. Gool, "Domain Adaptive Faster R-CNN for Object Detection in the Wild," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3339-3348, Jun. 2018.
  6. L. Jiao, F. Zhang, F. Liu, S. Yang, L. Li, Z. Feng, and Q. Rong, "A survey of deep learning-based object detection," IEEE Access, Vol. 7, pp.128837-128868, Sep. 2019. https://doi.org/10.1109/access.2019.2939201
  7. R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Rich feature hierarchies for accurate object detection and semantic segmentation," In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 580-587, Jun. 2014.
  8. R. Girshick, "Fast R-CNN," In Proceedings of the IEEE International conference on computer vision (ICCV), pp.1440-1448, Dec. 2015.
  9. K. Li, G. Wan, G. Cheng, L. Meng, and J. Han, "Object Detection in Optical Remote Sensing Images: A Survey and A New Benchmark," ISPRS Journal of Photogrammetry and Remote Sensing, Vol. 159, pp. 296-307, Jan. 2020. https://doi.org/10.1016/j.isprsjprs.2019.11.023
  10. U. Alganci, M. Soydas, and E. Sertel, "Comparative Research on Deep Learning Approaches for Airplane Detection from Very High-Resolution Satellite Images," Remote Sensing, Vol. 12, No.3, 458, Feb. 2020. https://doi.org/10.3390/rs12030458
  11. S. Ren, K. He, R. Girshick, and J. Sun, "Faster r-cnn: Towards real-time object detection with region proposal networks," IEEE transactions on pattern analysis and machine intelligence, Vol. 39, No. 6, pp.1137-1149, Jun. 2016. https://doi.org/10.1109/TPAMI.2016.2577031
  12. Naver Static Map API, https://apidocs.ncloud.com/ko/ai-naver/maps_static_map/ [accessed: Nov. 12, 2020]
  13. Kakao developers Vision OCR API, https://developers.kakao.com/docs/latest/ko/vision/dev-guide#ocr [accessed: Nov. 12, 2020]
  14. Kakao developers Local API, https://developers.kakao.com/docs/latest/ko/local/dev-guide#keyword [accessed: Nov. 12, 2020]
  15. Keras implementation of Faster R-CNN, https://github.com/inspace4u/keras-frcnn [accessed: Nov. 12, 2020]