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드론 영상 분석과 자료 증가 방법을 통한 건설 자재 수량 측정

Measurement of Construction Material Quantity through Analyzing Images Acquired by Drone And Data Augmentation

  • 문지환 (숭실대학교 융합소프트웨어학과) ;
  • 송누리 (숭실대학교 융합소프트웨어학과) ;
  • 최재갑 (숭실대학교 융합소프트웨어학과) ;
  • 박진호 (숭실대학교 소프트웨어학과) ;
  • 김계영 (숭실대학교 소프트웨어학과)
  • 투고 : 2019.07.09
  • 심사 : 2019.11.27
  • 발행 : 2020.01.31

초록

본 논문에서는 드론에 의하여 획득된 영상을 분석하여 건축자재의 수량을 측정하는 기술을 제안한다. 제안하는 기술은 드론 및 카메라 정보가 담겨있는 드론 로그와 영상 내 건축자재더미 종류와 영역을 예측하는 RCNN, 실제적인 수량 계산을 위한 사진측량법을 사용한다. 기존 연구에선 학습 데이터의 부족으로, 자재 종류 및 건축자재더미 영역 예측 정확도의 오류 범위가 컸다. 논문에서는 이러한 오류 범위를 줄이고 예측 안정성을 높이기 위해 자료 증가 방법으로 학습 데이터를 증가시킨다. 자료 증가는 학습 모델의 과적합을 막기 위해 회전에 의한 증가 방법만 사용한다. 수량 계산 방법으로는 Yaw, FOV 등의 드론 및 카메라 정보가 담겨있는 드론 로그와 영상 내 건축자재더미 영역을 찾고, 종류를 예측해 줄 RCNN 모델을 사용하고, 이 모든 정보를 종합해 논문에서 제안하는 수식에 적용하여 자재더미의 실제적인 수량을 계산한다. 제안하는 방법의 우수성은 실험을 통하여 확인한다.

This paper proposes a technique for counting construction materials by analyzing an image acquired by a Drone. The proposed technique use drone log which includes drone and camera information, RCNN for predicting construction material type, dummy area and Photogrammetry for counting the number of construction material. The existing research has large error ranges for predicting construction material detection and material dummy area, because of a lack of training data. To reduce the error ranges and improve prediction stability, this paper increases the training data with a method of data augmentation, but only uses rotated training data for data augmentation to prevent overfitting of the training model. For the quantity calculation, we use a drone log containing drones and camera information such as Yaw and FOV, RCNN model to find the pile of building materials in the image and to predict the type. And we synthesize all the information and apply it to the formula suggested in the paper to calculate the actual quantity of material pile. The superiority of the proposed method is demonstrated through experiments.

키워드

참고문헌

  1. H. Cholakkal, G. Sun, F. S. Khan, and L. Shao, "Object counting and instance segmentation with image-level supervision," In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 12397-12405), 2019.
  2. M.-R. Hsieh, Y.-L. Lin, and W. H. Hsu, "Drone-based object counting by spatially regularized regional proposal networks," In The IEEE International Conference on Computer Vision (ICCV), 2017.
  3. J. H. Moon, N. l. Song, J. G. Choi, J. H. Park, and G. Y. Kim, "Empirical study for counting same shaped building material quantity using UAV and deep learning," Spring Conferece of KIPS, Vol.26 No.1 pp.649-652, 2019.
  4. I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, Generative adversarial nets," In Proceedings of NIPS, pp.2672-2680, 2014.
  5. S. Belongie, J. Malik, and J. Puzicha, "Shape Matching and Object Recognition Using Shape Contexts," IEEE Transactions on Pattern Analysis & Machine Intelligence, Vol.24, pp.509-522, Apr. 2002. https://doi.org/10.1109/34.993558
  6. J. R. Uijlings, K. E. Sande, T. Gevers, and A. W. Smeulders, "Selective Search for Object Recognition," International Journal of Computer Vision, Vol.104, No.2, pp.154-171, Sep. 2013. doi: 10.1007/s11263-013-0620-5
  7. P. F. Felzenszwalb and D. P. Huttenlocher, "Pictorial Structures for Object Recognition," International Journal of Computer Vision, Vol.61, Issue 1, pp.55-79, Jan. 2005. doi: 10.1023/B:VISI.0000042934.15159.49
  8. R. Gopalan, R. Li, and R. Chellappa, "Domain adaptation for object recognition: An unsupervised approach," In Proc. of ICCV, pp.999-1006, 2011.
  9. Dominik Scherer, Adreas Muller, and Sven Behnke, "Evaluation of pooling operations in convolutional architectures for object recognition," In International Conference on Artificial Neural Networks, 2010.
  10. J. Ba, V. Mnih, and K. Kavukcuoglu, "Multiple object recognition with visual attention," ICLR, 2015.
  11. M. Liang and X. Hu, "Recurrent Convolutional Neural Network for Object Recognition," The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015, pp.3367-3375.
  12. A. Antoniou, A. Storkey, and H. Edwards. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340, 2017.
  13. Y. Xu, R. Jia, L. Mou, G. Li, Y. Chen, Y. Lu, and Z. Jin, "Improved relation classification by deep recurrent neural networks with data augmentation," CoRR, abs/1601.03651, 2016.
  14. E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, "AutoAugment: Learning Augmentation Policies from Data," 11 Apr. 2019, CVPR2019.
  15. Y. B. Brahme, and P. S. Kulkarni, "An Implementation of Moving Object Detection,Tracking and Counting Objects for Traffic Surveillance System," 2011 International Conference on Computational Intelligence and Communication Networks, Gwalior, pp.143-148, 2011. doi: 10.1109/CICN. 2011.28
  16. D. Beymer, "Person Counting Using Stereo," Proc. Workshop Human Motion, pp.127-133, 2000.
  17. D. Onoro-Rubio and R. J. Lopez-Sastre, "Towards perspective-free object counting with deep learning," In: Leibe B., Matas J., Sebe N., Welling M. (eds) Computer Vision - ECCV 2016. ECCV 2016. Lecture Notes in Computer Science, vol. 9911. Springer, Cham.
  18. M. S. Rahman and M. R. Islam, "Counting objects in an image by marker controlled watershed segmentation and thresholding," 2013 3rd IEEE International Advance Computing Conference (IACC), Ghaziabad, 2013, pp. 1251-1256. doi: 10.1109/IAdCC.2013.6514407
  19. T. Kobayashi, T. Hosaka, S. Mimura, T. Hayashi, and N. Otsu, "HLAC approach to automatic object counting," ECSIS Symposium on Bio‐inspired Learning and Intelligent Systems for Security, pp.40-45, 2008.
  20. J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, "You Only Look Once: Unified, Real-Time Object Detection," The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp.779-788, 2016.