KIPS Transactions on Software and Data Engineering (정보처리학회논문지:소프트웨어 및 데이터공학)

Korea Information Processing Society (한국정보처리학회)
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A Study on Machine Learning Algorithm Suitable for Automatic Crack Detection in Wall-Climbing Robot

벽면 이동로봇의 자동 균열검출에 적합한 기계학습 알고리즘에 관한 연구

  • 박재민 (한경대학교 전기전자제어공학과) ;
  • 김현섭 (한경대학교 전기전자제어공학과) ;
  • 신동호 (한경대학교 전기전자제어공학과) ;
  • 박명숙 (한경대학교 전기전자제어공학과) ;
  • 김상훈 (한경대학교 전기전자제어공학과)
  • Received : 2019.07.05
  • Accepted : 2019.09.06
  • Published : 2019.11.30
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Abstract

This paper is a study on the construction of a wall-climbing mobile robot using vacuum suction and wheel-type movement, and a comparison of the performance of an automatic wall crack detection algorithm based on machine learning that is suitable for such an embedded environment. In the embedded system environment, we compared performance by applying recently developed learning methods such as YOLO for object learning, and compared performance with existing edge detection algorithms. Finally, in this study, we selected the optimal machine learning method suitable for the embedded environment and good for extracting the crack features, and compared performance with the existing methods and presented its superiority. In addition, intelligent problem - solving function that transmits the image and location information of the detected crack to the manager device is constructed.

본 논문은 진공을 이용한 흡착방식과 바퀴형 이동방식을 사용하는 벽면 이동로봇의 구성과 이러한 임베디드 환경에 적합하고 기계학습에 기반한 벽면 균열 자동 검출 알고리즘의 성능 비교에 관한 연구이다. 임베디드 시스템 환경에서 객체 학습을 위해 YOLO 등 최근에 시도된 학습 방법들을 적용하여 성능을 비교, 검토하였으며 기존의 에지 검출 알고리즘들과도 성능을 비교하였다. 결국, 본 연구에서는 균열검출을 잘하며 임베디드 환경에도 적합한 최적의 기계학습방법을 선택하고 기존 방법과 성능을 비교하여 우수성을 제시하였다. 또한, 검출된 균열의 영상을 저장하고 위치 정보를 추정하여 균열에 대한 정보를 관리자 기기로 전송하는 지능적인 문제해결 기능을 구축하였다.

Keywords

References

  1. J. W. Kim, S. B. Kim, J. C. Park, and J. W. Nam, "Development of Crack Detection System with Unmanned Aerial Vehicles and Digital Image Processing," The 2015 World Congress on Advances in Structural Engineering and Mechanics (ASEM15), 2015.
  2. T. J. Yun, J. W. Jeon, B. Y. Ko, and H. K. Woo, "Crack Detection System for Exterior Wall in a Drone Camera Image using YOLO Deep Learning Technique," Proceedings of the Korean Society of Computer Information Conference, Vol.27, No.1, pp.303-304, 2019.
  3. J. W. Kim, Y. W. Jung, and H. C. Rhim, "Study on Structure Visual Inspection Technology using Drones and Image Analysis Techniques," Journal of the Korea Institute of Building Construction, Vol.17, pp.545-557, 2017. https://doi.org/10.5345/JKIBC.2017.17.6.545
  4. S. Y. An, J. H. Jang, C. S. Han, and P. H. Kim, "An Inspection System for Detection of Cracks on the Concrete Structures using a Mobile Robot," 21st International Symposium on Automation and Robotics in Construction (ISARC), Korea: Jeju, 21-25, 2004.
  5. Y. M. Eun, M. S. Kim, S. Y. Oh, S. W. Shin, and S. H. Kim, "Intelligent Robot for Wall Climbing," Journal of Information and Control Symposium, Vol.2012, No.10, pp.442-443, 2012.
  6. W. Morris and J. Xiao, "City-Climber: Development of a Novel Wall-Climbing Robot," J Stud Res., 1, pp.40-45, 2008.
  7. I. M. Koo, Y. K. Song, H. P. Moon, S. K. Park, and H. R. Choi, "Design and Control of Wall Climbing Robot Using Impeller," Journal of Intelligent & Robotic Systems, Vol.72, Issue. 1, pp.57-72, 2010. https://doi.org/10.1007/s10846-013-9820-z
  8. I. Abdel-Qader, O. Abudayyeh, and M. E. Kelly, "Analysis of Edge-Detection Techniques for Crack Identification in Bridges," Journal of Computing in Civil Engineering, Vol.17, No.4, pp.255-263, 2003. https://doi.org/10.1061/(ASCE)0887-3801(2003)17:4(255)
  9. Y. J. Cha and W. R. Choi, "Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks," Computer-Aided Civil and Infrastructure Engineering, Vol.32, No.5, pp.361-378, 2017. https://doi.org/10.1111/mice.12263
  10. J. Redmon and A. Farhadi, "YOLOv3: An Incremental Improvement", arXiv preprint arXiv:1804.02767, 2018.
  11. V. Nair and G. E. Hinton. "Rectified Linear Units Improve Restricted Boltzmann Machines," Proceedings of the 27th International Conference on Machine Learning (ICML-10), 2010.
  12. Mauriciomenegaz, "Understanding YOLO", Available at : https://hackernoon.com/understanding-yolo-f5a74bbc7967 (Accessed: 26 July 2019).
  13. N. G. Hyun, I. K. Lim, and J. K. Lee, "Location Estimation Method of Positioning System utilizing the iBeacon," Journal of the Korea Institute of Information and Communication Engineering, Vol.19, No.4, pp.925-932, 2015. https://doi.org/10.6109/jkiice.2015.19.4.925
  14. S. P. Kim, and N. G. Kim, "Point-based Location Estimation using WiFi Signal," The Journal of Korean Institute of Information Technology, Vol.10, No.1, pp.121-126, 2012.
  15. S. Madgwick, A. Harrison, and R. Vaidyanathan, "Estimation of IMU and MARG orientation using a gradient descent algorithm," 2011 IEEE International Conference on Rehabilitation Robotics. IEEE, 2011.
  16. K. S. Kim, and C. Y. Won, "Control Algorithm PMSM using Rectangular 2 Hall Sensors Compensated by Sensorless Control Method," 2011 International Conference on Electrical Machines and Systems. IEEE, 2012.
  17. Satyenrajpal, "Crack_dataset.tar.gz," Available at: https://drive.google.com/file/d/1kC60RGO3rcScVk7HY-s7tTMJeMbADf h1/view (Accessed: 20 March 2019).
  18. Caglar Firat Ozgenel, "Concrete Crack Images for Classification," Available at: http://dx.doi.org/10.17632/5y9wdsg2zt.1#file-c0d86f9f-852e-4d00-bf45-9a0e24e3b932 (Accessed:3 April 2019).
  19. T. S. Kim, D. I. Sin, K. H. Jin, and C. S. Jin, "Analysis of Crack in Concrete Structure Using Ultra-Sonic Technique," Journal of Research Institute of Industrial Technology, Vol.61, pp.55-62, 2002.