Tunnel and Underground Space (터널과지하공간)

Korean Society for Rock Mechanics and Rock Engineering (한국암반공학회)
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Automatic Fracture Detection in CT Scan Images of Rocks Using Modified Faster R-CNN Deep-Learning Algorithm with Rotated Bounding Box

회전 경계박스 기능의 변형 FASTER R-CNN 딥러닝 알고리즘을 이용한 암석 CT 영상 내 자동 균열 탐지

  • Pham, Chuyen (Dept. of Geo-Space Engineering, University of Science and Technology (UST)) ;
  • Zhuang, Li (Dept. of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology (KICT)) ;
  • Yeom, Sun (Dept. of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology (KICT)) ;
  • Shin, Hyu-Soung (Dept. of Future & Smart Construction Research, Korea Institute of Civil Engineering and Building Technology (KICT))
  • 추엔 팜 (한국과학기술연합대학원대학교 지반신공간공학과) ;
  • 장리 (한국건설기술연구원 미래스마트건설연구본부) ;
  • 염선 (한국건설기술연구원 미래스마트건설연구본부) ;
  • 신휴성 (한국건설기술연구원 미래스마트건설연구본부)
  • Received : 2021.09.14
  • Accepted : 2021.10.18
  • Published : 2021.10.31
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Abstract

In this study, we propose a new approach for automatic fracture detection in CT scan images of rock specimens. This approach is built on top of two-stage object detection deep learning algorithm called Faster R-CNN with a major modification of using rotated bounding box. The use of rotated bounding box plays a key role in the future work to overcome several inherent difficulties of fracture segmentation relating to the heterogeneity of uninterested background (i.e., minerals) and the variation in size and shape of fracture. Comparing to the commonly used bounding box (i.e., axis-align bounding box), rotated bounding box shows a greater adaptability to fit with the elongated shape of fracture, such that minimizing the ratio of background within the bounding box. Besides, an additional benefit of rotated bounding box is that it can provide relative information on the orientation and length of fracture without the further segmentation and measurement step. To validate the applicability of the proposed approach, we train and test our approach with a number of CT image sets of fractured granite specimens with highly heterogeneous background and other rocks such as sandstone and shale. The result demonstrates that our approach can lead to the encouraging results on fracture detection with the mean average precision (mAP) up to 0.89 and also outperform the conventional approach in terms of background-to-object ratio within the bounding box.

본 논문에서는 암석시료의 CT 촬영 이미지상의 균열을 자동으로 탐지하는 새로운 인공지능 딥러닝 기법을 제안한다. 본 제안 기법은 2단계 딥러닝 객체인식 알고르즘인 Faster R-CNN을 기반으로 회전 가능한 경계박스(bounding box) 개념을 도입하여 알고리즘을 개조하였다. 회전 경계박스의 도입은 관심 균열 영역 밖의 배경의 불균질성 및 균열의 크기와 형태에 영향을 받는 딥러닝 객체인식기법 상의 고유한 어려움을 극복하기 위한 핵심 역할을 한다. 본 회전형 경계박스의 사용은 일반적으로 사용되는 영상 수평축과 평행한 경계박스 사용의 경우와 비교하여 긴 형태의 균열 형상 특성에 매우 잘 부합된다. 즉, 좋지않은 영향을 끼치는 경계박스 내 균열 이외 배경영역의 비율을 최소화 시킬 수 있다. 이외에도, 회전 경계박스의 추가적인 이점은 인식된 균열의 방향에 따라 회전하여 추론되는 경계박스를 통해 균열의 방향과 길이에 대한 정보를 직접적으로 얻을 수 있다. 본 제안기법의 적용성을 검증하기 위하여, 이미지상에서 매우 불균질한 화강암 시료에 인공적으로 균열을 발생시킨 다수의 암석시료 영상을 딥러닝 학습에 사용하고 추론 성능 실험을 진행하였다. 그 외에도, 동일 조건에서 사암과 셰일 암석 시료에도 적용하여 검증하였다. 결론적으로, 제안된 기법을 통해 균열 객체 인식의 평균 추론정확도(mAP)값이 0.89 정도 수준의 우수한 추론 성능을 보였으며, 기존 기법에 비해 추론된 경계박스 내 균열과 배경 영역의 비율 측면에서 배경의 비율이 획기적으로 최소화되는 유리한 추론 검증 결과를 보였다.

Keywords

Acknowledgement

This research was supported by the research project "Development of environmental simulator and advanced construction technologies over TRL6 in extreme conditions" funded by KICT, and R & D project "Development of construction structure and long-term performance monitoring" (No. 20193210100050) funded by Korea Institute of Energy Technology Evaluation and Planning.

References

  1. Abadi, M., Agarwal, A., Barham, P., Brevdo, E., Chen, Z., Citro, C., Corrado, G. S., Davis, A., Dean, J., Devin, M., Ghemawat, S., Goodfellow, I., HaIp, A., Irving, G., Isard, M., Jia, Y., Jozefowicz, R., Kaiser, L., Kudlur, M., Levenberg, J., Mane, D., Monga, R, Moore, S., Murray, D., Olah, C., Schuster, M., Shlens, J., Steiner, B., Sutskever, I., Talwar, K., Tucker, P., Vanhoucke, V., Vasudevan, V., Viegas, F., Vinyals, O., Warden, P., Wattenberg, M., Wicke, M., Yu, Y., & Zheng, X. (2015), "TensorFlow: Large-scale machine learning on heterogeneous systems", Software available from http://tensorflow.org/.
  2. Andra, H., Combaret, N., Dvorkin, J., Glatt, E., Han, J., Kabel, M., Keehm, Y., Krzikalla, F., Lee M., and Madonna, C. (2013), "Digital rock physics benchmarks-Part I: Imaging and segmentation", Comput Geosci. 50, pp. 25-32. https://doi.org/10.1016/j.cageo.2012.09.005
  3. Crandall D, Moore J, Gill M, Stadelman M. (2017), "CT scanning and flow measurements of shale fractures after multiple shearing events", Int J Rock Mech Min Sci. 100, pp.177-187. https://doi.org/10.1016/j.ijrmms.2017.10.016
  4. Girshick, R (2015), Fast R-CNN. In: ICCV.
  5. He, K., Gkioxari, G., Dollar, P., Girshick, R B. (2017), "Mask R-CNN", CoRR abs/1703.06870 (2017). arXiv:1703.06870 http://arxiv.org/abs/1703.06870
  6. He, K., Zhang, X., Ren, S., and Sun, J. (2015), "Delving deep into rectifiers: Surpassing human-level performance on ImageNet classification", In: Proceedings of the IEEE international conference on computer vision, IEEE, NY, USA, pp. 1026-1034.
  7. Ketcham, R. A., and CarIson, W. D. (2001), "Acquisition, optimization and interpretation of X-ray computed tomographic imagery: applications to the geosciences", Comput. Geosci. Geosc. 27, pp. 381-400. https://doi.org/10.1016/S0098-3004(00)00116-3
  8. Kling, T., Huo, D., Schwarz, J.O., Enzmann, F., Benson, S., Blum, P. (2016), "Simulating stress-dependent fluid flow in a fracturedcore sample using real-time X-ray CT data", Solid Earth, 7, pp. 1109-1124. https://doi.org/10.5194/se-7-1109-2016
  9. Kyle, J. R, and Ketcham, R. A. (2015), "Application of high resolution X-ray computed tomography to mineral deposit origin, evaluation, and processing", Ore Geol. Rev. 65, pp. 821-839. https://doi.org/10.1016/j.oregeorev.2014.09.034
  10. Lin, T., Dollar, P., Girshick, R.B., He, K., Hariharan, B., Belongie, S. J. (2017), "Feature pyramid networks for object detection", CVPR.
  11. Lin, T., Maire, M, Belongie, S. J., Hays, J., Perona, P., Ramanan, D., Dollar, P., Zitnick C. L. (2014), "Microsoft COCO: common objects in context", in Computer Vision - ECCV 2014 - 13th European Conference, Zurich,Switzerland, September 6-12, 2014, Proceedings, Part V, ser. LectureNotes in Computer Science, vol. 8693. Springer, pp. 740-755.
  12. Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu C-Y. (2016), "SSD: Single Shot MultiBox Detector", ArXiv151202325 Cs. 2016; 9905:21-37.
  13. Otsu, N., 1979. A threshold selection method from gray-level histograms. IEEE Trans. Sys. Man. Cyber. 9 (1), 62-66. doi: 10.1109/TSMC.1979.4310076.
  14. Redmon, J., DivvaIa, S., Girshick, R, Farhadi, A. (2016), "You only look once: Unified, real-time object detection", in IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  15. Roo, S., He, K., Girshick, R, Sun, J. (2015), "Faster R-CNN: Towards real-time object detection with region proposal networks", in: NIPS.
  16. Ronneberger, P. Fischer, Brox, T. (2015), "U-Net: Convolutional Networks for Biomedical Image Segmentation", MICCAI, Springer, LNCS, 9351, pp. 234-241.
  17. Schmitt, M., Halisch, M., Muller, C., and Fernandes, C. P. (2016), "Classification and quantification of pore shapes in sandstone reservoir rocks with 3-D X-ray microcomputed tomography", Solid Earth. 7, pp. 285-300. https://doi.org/10.5194/se-7-285-2016
  18. Voorn, M., Exner, U., and Rath, A. (2013), "Multiscale Hessian fracture filtering for the enhancement and segmentation of narrow fractures in 3D image data", Comput. Geosci. 57, pp. 44-53. https://doi.org/10.1016/j.cageo.2013.03.006
  19. Vu, T. X., Jang, H., Pharo, T. X., and Yoo, C. D. (2019), "Cascade RPN: Delving into high-quality region proposal network with adaptive convolution", in Proc. NIPS, pp. 1-11.
  20. Wennberg, O. P., Rennan, L., and Basquet, R. (2009), "Computed tomography scan imaging of natural open fractures in a porous rock; geometry and fluid flow", Geophys. Prospect 57, pp. 239-249. https://doi.org/10.1111/j.1365-2478.2009.00784.x
  21. Yen, J.Y., 1970. An algorithm for finding shortest routes from all source nodes to a given destination in general networks. Quarterly of Applied Mathematics. 27 (4), 526-530. doi:10.1090/qam/253822.