Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching
Abstract
:1. Introduction
2. Structural Exterior Image Stitching through Automated Background Removal
2.1. Automated Background Removal Using Deep Learning-Based Depth Estimation
2.2. Optimal Image Selection for Cost-Effective Digital Image Stitching
2.3. Mesh-Based Digital Image Stitching for Structural Exterior Map Establishment
3. Experimental Validation
4. Discussion
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Kang, D.; Cha, Y.J. Autonomous UAVs for structural health monitoring using deep learning and an ultrasonic beacon system with geo-tagging. Comput. Aided Civ. Inf. 2018, 33, 885–902. [Google Scholar] [CrossRef]
- Bae, H.; Jang, K.; An, Y.K. Deep super resolution crack network (SrcNet) for improving computer vision-based automated crack detectability in in situ bridges. Struct. Health Monit. 2020, 1–15. [Google Scholar] [CrossRef]
- Kerle, N.; Nex, F.; Gerke, M.; Duarte, D.; Vetrivel, A. UAV-based structural damage mapping: A review. ISPRS Int. J. Geo-Inf. 2020, 9, 14. [Google Scholar] [CrossRef] [Green Version]
- Zhu, Z.; German, S.; Brilakis, I. Detection of large-scale concrete columns for automated bridge inspection. Autom. Constr. 2010, 19, 1047–1055. [Google Scholar] [CrossRef]
- Jahanshahi, M.R.; Masri, S.F.; Sukhatme, G.S. Multi-image stitching and scene reconstruction for evaluating defect evolution in structures. Struct. Health Monit. 2011, 10, 643–657. [Google Scholar] [CrossRef] [Green Version]
- Zhu, Z.H.; Fu, J.Y.; Yang, J.S.; Zhang, X.M. Panoramic image stitching for arbitrarily shaped tunnel lining inspection. Comput. Aided Civ. Inf. 2016, 31, 936–953. [Google Scholar] [CrossRef]
- Yoon, S.; Gwon, G.H.; Lee, J.H.; Jung, H.J. Three-dimensional image coordinate-based missing region of interest area detection and damage localization for bridge visual inspection using unmanned aerial vehicles. Struct. Health Monit. 2020, 1–14. [Google Scholar] [CrossRef]
- Morgenthal, G.; Hallermann, N. Quality assessment of unmanned aerial vehicle based visual inspection of structures. Adv. Struct. Eng. 2014, 17, 289–302. [Google Scholar] [CrossRef]
- Xie, R.; Yao, J.; Liu, K.; Lu, X.; Liu, Y.; Xia, M.; Zeng, Q. Automatic multi-image stitching for concrete bridge inspection by combining point and line feature. Autom. Constr. 2018, 90, 265–280. [Google Scholar] [CrossRef]
- Won, J.; Park, J.W.; Shim, C.; Park, M.W. Bridge-surface panoramic-image generation for automated bridge-inspection using deepmatching. Struct. Health Monit. 2020, 1, 15. [Google Scholar] [CrossRef]
- Jang, K.; An, Y.K.; Kim, B.; Cho, S. Automated crack evaluation of a high-rise bridge pier using a ring-type climbing robot. Comput. Aided Civ. Inf. 2021, 36, 14–29. [Google Scholar] [CrossRef]
- Yang, T.; Li, J.; Yu, J.; Wang, S.; Zhang, Y. Diverse scene stitching from a large-scale aerial video dataset. Remote Sens. 2015, 7, 6932–6949. [Google Scholar] [CrossRef] [Green Version]
- Bang, S.; Kim, H.; Kim, H. UAV-based automatic generation of high-resolution panorama at a construction site with a focus on preprocessing for image stitching. Autom. Constr. 2017, 84, 70–80. [Google Scholar] [CrossRef]
- Bu, S.; Zhao, Y.; Wan, G.; Liu, Z. Map2DFusion: Real-time incremental UAV image mosaicking based on monocular SLAM. In Proceedings of the IEEE/RSJ International Conference of Intelligent Robots and Systems (IROS 2016), Daejeon, Korea, 9–14 October 2016; pp. 4564–4571. [Google Scholar]
- Xin, Y.; Hou, J.; Dong, L.; Ding, L. A self-adaptive optical flow method for the moving object detection in the video sequences. Optik 2014, 125, 5690–5694. [Google Scholar] [CrossRef]
- Supreeth, H.S.G.; Patil, C.M. Efficient multiple moving object detection and tracking using combined background subtraction and clustering. Signal Image Video Process. 2018, 12, 1097–1105. [Google Scholar] [CrossRef]
- Fang, W.; Ding, Y.; Zhang, F.; Sheng, V.S. DOG: A new background removal for object recognition from images. Neurocomputing. 2019, 361, 85–91. [Google Scholar] [CrossRef]
- Godard, C.; Mac Aodha, O.; Firman, M.; Brostow, G.J. Digging into self-supervised monocular depth estimation. In Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV 2019), Seoul, Korea, 27 October–2 November 2019; pp. 3828–3838. [Google Scholar]
- Otsu, N. A threshold selection method from gray level histograms. IEEE Trans. Syst. Man Cybern. 1979, 9, 62–66. [Google Scholar] [CrossRef] [Green Version]
- Lowe, D.G. Object recognition from local scale-invariant features. In Proceedings of the International Conference on Computer Vision (ICCV 1999), Corfu, Greece, 20–25 September 1999; Volume 2, pp. 1150–1157. [Google Scholar]
- Chen, Y.S.; Chuang, Y.Y. Natural image stitching with global similarity prior. In Proceedings of the European Conference on Computer Vision (ECCV 2016), Amsterdam, The Netherlands, 8–16 October 2016; pp. 186–201. [Google Scholar]
- Zaragoza, J.; Chin, T.J.; Brown, M.S.; Suter, D. As-projective-as-possible image stitching with moving DLT. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2013), Portland, OR, USA, 25–27 June 2013; pp. 2339–2349. [Google Scholar]
- Schaefer, S.; McPhail, T.; Warren, J. Image deformation using moving least squares. In Proceedings of the International Conference on Computer Graphics and Interactive Techniques (SIGGRAPH 2006), Boston, MA, USA, 30 July–4 August 2006; pp. 533–540. [Google Scholar]
- Chen, Q. VR: An image-based approach to virtual environment navigation. In Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH1995), Los Angeles, CA, USA, 29–38 August 1995. [Google Scholar]
Raw Images | Optimal Images | |
---|---|---|
Number of images | 118 | 35 |
Computational time | 9 h 13 min 47 s | 1 h 18 min 57 s |
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Kang, M.S.; An, Y.-K. Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching. Appl. Sci. 2021, 11, 3339. https://doi.org/10.3390/app11083339
Kang MS, An Y-K. Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching. Applied Sciences. 2021; 11(8):3339. https://doi.org/10.3390/app11083339
Chicago/Turabian StyleKang, Myung Soo, and Yun-Kyu An. 2021. "Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching" Applied Sciences 11, no. 8: 3339. https://doi.org/10.3390/app11083339
APA StyleKang, M. S., & An, Y.-K. (2021). Deep Learning-Based Automated Background Removal for Structural Exterior Image Stitching. Applied Sciences, 11(8), 3339. https://doi.org/10.3390/app11083339