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http://dx.doi.org/10.3745/KTSDE.2020.9.1.33

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

Moon, Ji-Hwan (숭실대학교 융합소프트웨어학과)
Song, Nu-Lee (숭실대학교 융합소프트웨어학과)
Choi, Jae-Gab (숭실대학교 융합소프트웨어학과)
Park, Jin-Ho (숭실대학교 소프트웨어학과)
Kim, Gye-Young (숭실대학교 소프트웨어학과)
Publication Information
KIPS Transactions on Software and Data Engineering / v.9, no.1, 2020 , pp. 33-38 More about this Journal
Abstract
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.
Keywords
Drone; UAV; RCNN; Deep Learning; Counting Number; Construction Material;
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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.   DOI
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   DOI
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   DOI
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 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
13 A. Antoniou, A. Storkey, and H. Edwards. Data augmentation generative adversarial networks. arXiv preprint arXiv:1711.04340, 2017.
14 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.
15 E. D. Cubuk, B. Zoph, D. Mane, V. Vasudevan, and Q. V. Le, "AutoAugment: Learning Augmentation Policies from Data," 11 Apr. 2019, CVPR2019.
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.