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http://dx.doi.org/10.22640/lxsiri.2018.48.2.213

Application Research on Obstruction Area Detection of Building Wall using R-CNN Technique  

Kim, Hye Jin (Department of Advanced Technology Fusion, Konkuk University)
Lee, Jeong Min (Shinhan Aerial Survey Research Institute)
Bae, Kyoung Ho (Shinhan Aerial Survey Research Institute)
Eo, Yang Dam (Department of Advanced Technology Fusion, Konkuk University)
Publication Information
Journal of Cadastre & Land InformatiX / v.48, no.2, 2018 , pp. 213-225 More about this Journal
Abstract
For constructing three-dimensional (3D) spatial information occlusion region problem arises in the process of taking the texture of the building. In order to solve this problem, it is necessary to investigate the automation method to automatically recognize the occlusion region, issue it, and automatically complement the texture. In fact there are occasions when it is possible to generate a very large number of structures and occlusion, so alternatives to overcome are being considered. In this study, we attempt to apply an approach to automatically create an occlusion region based on learning by patterning the blocked region using the recently emerging deep learning algorithm. Experiment to see the performance automatic detection of people, banners, vehicles, and traffic lights that cause occlusion in building walls using two advanced algorithms of Convolutional Neural Network (CNN) technique, Faster Region-based Convolutional Neural Network (R-CNN) and Mask R-CNN. And the results of the automatic detection by learning the banners in the pre-learned model of the Mask R-CNN method were found to be excellent.
Keywords
3D Spatial information; Occlusion area; Deep-learning; R-CNN;
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Times Cited By KSCI : 2  (Citation Analysis)
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1 Kim SS, Kim BG. 2002. Adjustment of texture image for construction of a 3D virtual city. Journal of the Korean Society for Geospatial Information Science. 10(2):49-56.
2 Kim HJ, Han YK, Choi JW, Kim YI. 2009. High resolution satellite image classification enhancement using restortation of buildin shadow and occlusion. The Korean Society of Remote Sensing. Proceedings of the KSRS Conference (Mar):13-17.
3 Park JH, Suh YC. 2017. Calculation of the Duration of Sunshine Using a Three-Dimensional Spatial Information Open Platform. Journal of the Korean Association of Geographic Information Studies. 20(3):80-89.   DOI
4 Shin DK, Park CW, Park JW, Kim YM, Park KT, Moon YS. 2011. Feature based Depth Map Generation for Compensation of Occlusion Region using Disparity Estimation. The Journal of Korean Institute of Information Technology. 9(5):217-230.
5 Lee JS, Lee IG. 2018. The 3D Modeling Data Production Method Using Drones Photographic Scanning Technology. Journal of the Korea Institute of Information and Communication Engineering. 22(6):874-880.   DOI
6 Jung SH, Lee JK. 2008. Application of Photorealitstic Modeling and Visualization Using Digital Image Data in 3D GIS. Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography. 26(1):73-83.
7 Cho MH, Seo JT, Lee CY, Par YJ. 2011. The Realization of Disaster Information using Virtual Simulation based on 3D Spatial Information. Journal of the Korean Society of Hazard Mitigation. 11(5):175-184.   DOI
8 Girshick R, Donahue J, Darrell T, Malik J. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. 2014 IEEE Conference on Computer Vision and Pattern Recognition. pp.580-587.
9 Gruen A, Huang X, Qin R, Du T, Fang W, Boavida J, Oliveira A. 2013. Joint processing of UAV imagery and terrestrial mobile mapping system data for very high resolution city modeling. Remote Sens Spatial Inf Sci. pp.175-182.
10 Hammoudi K, Dornaika F, Soheilian B, Vallet B, McDonald J, Paparoditis N. 2012. Recovering occlusion-free textured 3D maps of urban facades by a synergistic use of terrestrial images, 3D point clouds and area-based information. Procedia Engineering. 41(2012): 971-980.   DOI
11 He K, Gkioxari G, Dollar P, Girshick R. 2017. Mask R-CNN. IEEE International Conference on Computer Vision (ICCV). pp.2980-2988.
12 Ho WT, Lim HW, Tay YH. 2009. Two-stage license plate detection using gentle Adaboost and SIFT-SVM. Intelligent Information and Database Systems. pp.109-114.
13 Kim HT, Kim SB, Go JS, Eo YD, Lee BK. 2010. Building 3D Geospatial Information using Airborne Multi-Looking Digital Camera System. Journal of Convergence Information Technology. 5(1):15-22.   DOI
14 Levi D, Garnett N, Fetaya E. 2015. StixelNet: A Deep Convolutional Network for Obstacle Detection and Road Segmentation. British Machine Vision Conference 2015. pp.109.1-109.12.
15 Papageorgiou CP, Oren M, Poggio T. 1998. A general framework for object detection. Sixth International Conference on Computer Vision. pp.555-562.
16 Powers, David MW. 2011. Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. Journal of Machine Learning Technologies. 2(1):37-63.
17 Sivaraman S, Trivedi MM. 2013. A review of recent developments in vision-based vehicle detection. Intelligent Vehicles Symposium (IV). pp.310-315.
18 Remondino F, Barazzetti L, Nex F, Scaioni M, Sarazzi D. 2011. UAV photogrammetry for mapping and 3D modeling-Current status and future perspectives. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XXXVIII-1(C22):25-31.
19 Ren S, He K, Girshick R, Sun J. 2015. Faster r-cnn: Towards real-time object detection with region proposal networks. Neural Information Processing Systems (NIPS). pp.91-99.
20 Simard PY, Steinkraus D, Platt JC. 2003. Best practices for convolutional neural networks applied to visual document analysis. Document Analysis and Recognition. p.958.
21 Sivaraman S, Trivedi MM. 2014. Active learning for on-road vehicle detection: A comparative study. Machine vision and applications. 25(3): 599-611.   DOI
22 Xiao J, Fang T, Zhao P, Lhuillier M, Quan L. 2009. Image-based street-side city modeling. In ACM transactions on Graphics (TOG). 28(5):114.   DOI