Detection of collapsed buildings with the aerial images captured from UAV



In this paper, we present a method of detecting the collapsed buildings with the aerial images which are captured by an unmanned aerial vehicle (UAV) for the postseismic evaluation. Different from the conventional methods that apply the satellite images or the high-altitude UAV for the coarse disaster evaluation over large area, the purpose of this work is to achieve the accurate detection of collapsed buildings in small area from low altitude. By combining the motion and appearance features of collapsed buildings extracted from successive aerial images, each pixel in the input image will be measured by a statistical method where the background pixels will be penalized and the ones of collapsed buildings will be assigned with high value. The candidates of collapsed buildings will be established by integrating the extracted feature points into local groups with the online clustering algorithm. To reduce the false alarm caused by the complex background noise, each predicted candidate will be further verified by the temporal tracking framework where both the trajectory and the appearance of a candidate will be measured. The candidate of collapsed buildings that can survive through long time will be considered as true positive, otherwise rejected as a false alarm. Through extensive experiments, the efficiency and the effectiveness of proposed algorithm have been proved.



本文提出了一种依靠低空无人机航拍图像进行坍塌建筑物自动识别的实时灾情评估方法。有别于传统的广域灾情粗略评估系统, 本方法依靠低空无人机实现了对村镇级别小范围区域的坍塌建筑物实时自动识别。本文创新点包括: 1) 结合航拍图像中每个像素点的外形和运动特征, 利用统计方法提取出坍塌建筑物上的有效特征点并抑制背景噪声; 2) 通过在线聚类算法实时提取出疑似坍塌建筑物区域; 3) 通过时空追踪算法对疑似区域进一步筛选, 排除误报结果。

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  1. 1

    Viola P, Jones M. Robust real-time object detection. Int J Comput Vis, 2004, 57: 137–154

    Article  Google Scholar 

  2. 2

    Dalal N, Triggs B. Histograms of oriented gradients for human detection. In: Proceeding of IEEE Conference of Computer Vision abd Pattern Recognition, San Diego, 2005. 886–893

    Google Scholar 

  3. 3

    Felzenszwalb P F, Girshick R B, McAllester D, et al. Object detection with discriminatively trained part-based models. IEEE Trans Patt Anal Mach Intell, 2010, 32: 1627–1645

    Article  Google Scholar 

  4. 4

    Felzenszwalb P F, Girshick R B, McAllester D. Cascade object detection with deformable part models. In: Proceeding of IEEE Conference of Computer Vision and Pattern Recognition, San Francisco, 2010. 2241–2248

    Google Scholar 

  5. 5

    Hua C S, Makihara Y, Yagi Y. Pedestrian detection by using a spatio-temporal histogram of oriented gradients. IEICE Trans Inf Syst, 2013, E96-D: 1376–1386

    Article  Google Scholar 

  6. 6

    Tong X H, Hong Z H, Liu S J, et al. Building-damage detection using pre-and post-seismic high-resolution satellite stereo imagery: a case study of the may 2008 wenchuan earthquake. ISPRS J Photogramm Remot Sens, 2012, 68: 13–27

    Article  Google Scholar 

  7. 7

    Tong X H, Lin X F, Feng T T, et al. Use of shadows for detection of earthquake-induced collapsed buildings in high-resolution satellite imagery. ISPRS J Photogramm Remot Sens, 2013, 79: 53–67

    Article  Google Scholar 

  8. 8

    Murayama Y, Tashiro T, Yamazaki F. Detection of collapsed buildings after the 2007 niigata chuetshuoki earthquake based on digital surface model constructed from aerial images. In: Proceeding of the 2nd International Symposium on Advances in Urban Safety, Kobe, 2010. 319–324

    Google Scholar 

  9. 9

    Rezaeian M. Automatic classification of collapsed buildings using stereo aerial images. Int J Comput Appl, 2012, 46: 35–42

    Google Scholar 

  10. 10

    Elberink S O, Shoko M, Fathi S A, et al. Detection of collapsed buildings by classifying segmented airborne laser scanner data. In: Proceeding of International Archives of Photogrammetry and Remote Sensing and Spatial Information Science, Calgary, 2011. 307–312

    Google Scholar 

  11. 11

    Khoshelham K, Elberink S O. Role of dimensionality reduction in segment-based classification of damaged building roofs in airborne laser scanning data. In: Proceeding of 4th International Conference on Geographic Object-Based Image Analysis, Riode Janeiro, 2012. 372–377

    Google Scholar 

  12. 12

    Suner E, Turker M. Building damage detection from post-earthquake aerial imagery using building grey-value and gradient orientation analysis. In: Proceeding of 2nd International Conference of Recent Advance in Space Technology, Istanbul, 2005. 577–582

    Google Scholar 

  13. 13

    Li L, Zhang B, Wu Y. Fusing spectral and texture information for collapsed buildings detection in airborne image. In: Proceeding of IEEE Internal Geoscience and Remote Sensing Symposium, Munich, 2012. 186–189

    Google Scholar 

  14. 14

    Dai L, Qi J T, Wu C, et al. Magnetic compass error analysis and calibration for rotorcraft flying robot, Robot, 2012, 34: 418–424

    Article  Google Scholar 

  15. 15

    Qi J T, Han J D. Application of wavelets transform to fault detection in rotorcraft UAV sensor failure. J Bion Eng, 2007, 4: 265–270

    Article  Google Scholar 

  16. 16

    Qi J T, Song D, Han J D, et al. KF-based adaptive UKF algorithm and its application for rotorcraft UAV actuator failure estimation. Int J Adv Robot Syst, 2012, 9: 1–9

    Google Scholar 

  17. 17

    Comaniciu D, Ramesh V, Meer P. The variable bandwidth mean shift and data-driven scale selection. In: Proceeding of IEEE International Conference Computer Vision, Vancouver, 2001. 438–445

    Google Scholar 

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Correspondence to Juntong Qi.

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Hua, C., Qi, J., Shang, H. et al. Detection of collapsed buildings with the aerial images captured from UAV. Sci. China Inf. Sci. 59, 32102 (2016).

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  • collapsed buildings
  • aerial images
  • UAV
  • online detection
  • temporal tracking


  • 坍塌建筑物
  • 航拍图像
  • 低空无人机
  • 在线检测
  • 时空追踪