Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
권호 표지
DOI QR코드

DOI QR Code

Detection Ability of Occlusion Object in Deep Learning Algorithm depending on Image Qualities

영상품질별 학습기반 알고리즘 폐색영역 객체 검출 능력 분석

  • 이정민 ((주)신한항업 기업부설연구소) ;
  • 함건우 ((주)신한항업 기업부설연구소) ;
  • 배경호 ((주)신한항업 기업부설연구소) ;
  • 박홍기 (가천대학교 토목환경공학과)
  • Received : 2019.09.23
  • Accepted : 2019.09.26
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

Abstract

The importance of spatial information is rapidly rising. In particular, 3D spatial information construction and modeling for Real World Objects, such as smart cities and digital twins, has become an important core technology. The constructed 3D spatial information is used in various fields such as land management, landscape analysis, environment and welfare service. Three-dimensional modeling with image has the hig visibility and reality of objects by generating texturing. However, some texturing might have occlusion area inevitably generated due to physical deposits such as roadside trees, adjacent objects, vehicles, banners, etc. at the time of acquiring image Such occlusion area is a major cause of the deterioration of reality and accuracy of the constructed 3D modeling. Various studies have been conducted to solve the occlusion area. Recently the researches of deep learning algorithm have been conducted for detecting and resolving the occlusion area. For deep learning algorithm, sufficient training data is required, and the collected training data quality directly affects the performance and the result of the deep learning. Therefore, this study analyzed the ability of detecting the occlusion area of the image using various image quality to verify the performance and the result of deep learning according to the quality of the learning data. An image containing an object that causes occlusion is generated for each artificial and quantified image quality and applied to the implemented deep learning algorithm. The study found that the image quality for adjusting brightness was lower at 0.56 detection ratio for brighter images and that the image quality for pixel size and artificial noise control decreased rapidly from images adjusted from the main image to the middle level. In the F-measure performance evaluation method, the change in noise-controlled image resolution was the highest at 0.53 points. The ability to detect occlusion zones by image quality will be used as a valuable criterion for actual application of deep learning in the future. In the acquiring image, it is expected to contribute a lot to the practical application of deep learning by providing a certain level of image acquisition.

정보화 사회로 진입하면서 공간정보의 중요성은 급격하게 부각되고 있다. 특히 스마트시티, 디지털트윈과 같은 Real World Object의 3차원 공간정보 구축 및 모델링은 중요한 핵심기술로 자리매김하고 있다. 구축된 3차원 공간정보는 국토관리, 경관분석, 환경 및 복지 서비스 등 다양한 분야에서 활용된다. 영상기반의 3차원 모델링은 객체 벽면에 대한 텍스처링을 생성하여 객체의 가시성과 현실성을 높이고 있다. 하지만 이러한 텍스처링은 영상 취득 당시의 가로수, 인접 객체, 차량, 현수막 등의 물리적 적치물에 의해 필연적으로 폐색영역이 발생한다. 이러한 폐색영역은 구축된 3차원 모델링의 현실성과 정확성 저하의 주요원인이다. 폐색영역 해결을 위한 다양한 연구가 수행되고 있으며, 딥러닝을 이용한 폐색영역 검출 및 해결방안에 대한 연구가 수행되고 있다. 딥러닝 알고리즘 적용한 폐색영역 검출 및 해결을 위해서는 충분한 학습 데이터가 필요하며, 수집된 학습 데이터 품질은 딥러닝의 성능 및 결과에 직접적인 영향을 미친다. 따라서 본 연구에서는 이러한 학습 데이터의 품질에 따라 딥러닝의 성능 및 결과를 확인하기 위하여 다양한 영상품질을 이용하여 영상의 폐색영역 검출 능력을 분석하였다. 폐색을 유발하는 객체가 포함된 영상을 인위적이고 정량화된 영상품질별로 생성하여 구현된 딥러닝 알고리즘에 적용하였다. 연구결과, 밝기값 조절 영상품질은 밝은 영상일수록 0.56 검출비율로 낮게 나타났고 픽셀크기와 인위적 노이즈 조절 영상품질은 원본영상에서 중간단계의 비율로 조절된 영상부터 결과 검출비율이 급격히 낮아지는 것을 확인할 수 있었다. F-measure 성능평가 방법에서 노이즈 조절한 영상품질 변화가 0.53으로 가장 높게 나타났다. 연구결과로 획득된 영상품질별에 따른 폐색영역 검출 능력은 향후 딥러닝을 실제 적용을 위한 귀중한 기준으로 활용될 것이다. 영상 취득 단계에서 일정 수준의 영상 취득과 노이즈, 밝기값, 픽셀크기 등에 대한 기준을 마련함으로써 딥러닝을 실질적인 적용에 많은 기여가 예상된다.

Keywords

References

  1. Choi, H.S. and E.M. Kim. 2015. Detection of road signs region and recognition of directional information. Proceeding of Korean Society for Geospatial Information Science 197-198
  2. Girshick, R. 2015. Fast R-CNN. Proceedings of the IEEE international conference on computer vision 1440-1448.
  3. Girshick, R., J. Donahue, T. Darrell and J. Malik. 2014. Rich feature hierarchies for accurate object detection and semantic segmentation. Proceedings of the IEEE conference on computer vision and pattern recognition 580-587.
  4. Hariharan, B., P. Arbelaez, R. Girshick and J. Malik. 2014. Simultaneous detection and segmentation. In European Conference on Computer Vision 297-312.
  5. Hinton, G.E. and R.R. Salakhutdinov. 2006. Reducing the Dimensionality of Data with Neural Networks. science 313(5786):504-507. https://doi.org/10.1126/science.1127647
  6. Kim, J.K. and J,W. Choi. 2017. Object Detection from Camera, Lidar Fusion based on Deep Learning, Proceeding of Communications and Networks 270-271
  7. Krizhevsky, A., I. Sutskever and G.E. Hinton. 2012. ImageNet Classification with Deep Convolutional Neural Networks. In Advances in neural information processing systems 1097-1105.
  8. Kwon, S.I. and E.M. Kim. 2019. Recognition of Flat Type Signboard using Deep Learning. Journal of The Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography 37(4):219-231 https://doi.org/10.7848/KSGPC.2019.37.4.219
  9. LeCun, Y., Y. Bengio and G. Hinton. 2015. Deep learning. nature, 521(7553):436. https://doi.org/10.1038/nature14539
  10. Lowe, D.G. 2004. Distinctive Image Features from Scale-Invariant Keypoints. International journal of computer vision, 60(2):91-110. https://doi.org/10.1023/B:VISI.0000029664.99615.94
  11. Mees, O., A. Eitel and W. Burgard. 2016. Choosing Smartly:Adaptive Multimodal Fusion for Object Detection in Changing Enviroments. 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems 151-156.
  12. Park, S.J., S.M. Choi, h.j. Lee and j.b. Kim. 2016. Spatial analysis using R based Deep Learnin. Asia-pacific Journal of Multimedia Services Convergent with Art, Humanities, and Sociology. 6(4):1-8
  13. Ren, S., K. He, R. Girshick and J. Sun. 2015. Faster R-CNN: Towards real-time object detection with region proposal networks. In Advances in neural information processing systems 91-99.
  14. Sa Gong, H.S. and S.Y. Lim. 2018. Digitla Twin Spatial Construction Strategy Leading 4th industry. KRIHS POLICY BRIEF (661):1-6
  15. Yoo, E.J. and D.C. Lee. 2016. Detection and Recovery of Occlusion Areas caused by Building Sidewalls in Aerial Photos. Proceeding of The Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography 2016(4):156-158
  16. Zhou, B., A. Lapedriza, J. Xiao, A. Torralba and A. Oliva. 2014. Learning deep features for scene recognition using places database. In Advances in neural information processing systems 487-495.