The Journal of The Korea Institute of Intelligent Transport Systems (한국ITS학회 논문지)

The Korea Institute of Intelligent Transport Systems (한국ITS학회)
권호 표지
DOI QR코드

DOI QR Code

Road Crack Detection based on Object Detection Algorithm using Unmanned Aerial Vehicle Image

드론영상을 이용한 물체탐지알고리즘 기반 도로균열탐지

  • Received : 2019.10.25
  • Accepted : 2019.11.05
  • Published : 2019.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

This paper proposes a new methodology to recognize cracks on asphalt road surfaces using the image data obtained with drones. The target section was Yuseong-daero, the main highway of Daejeon. Furthermore, two object detection algorithms, such as Tiny-YOLO-V2 and Faster-RCNN, were used to recognize cracks on road surfaces, classify the crack types, and compare the experimental results. As a result, mean average precision of Faster-RCNN and Tiny-YOLO-V2 was 71% and 33%, respectively. The Faster-RCNN algorithm, 2Stage Detection, showed better performance in identifying and separating road surface cracks than the Yolo algorithm, 1Stage Detection. In the future, it will be possible to prepare a plan for building an infrastructure asset-management system using drones and AI crack detection systems. An efficient and economical road-maintenance decision-support system will be established and an operating environment will be produced.

본 연구에서는 대전광역시 주요 간선도로인 유성대로를 대상으로 드론을 통해 취득한 노면 영상데이터를 기반으로 물체탐지알고리즘(Object Detection algorithm) 가운데 Tiny-YOLO-V2와 Faster-RCNN을 활용하여 아스팔트 도로노면의 균열을 인식, 균열유형을 구분하고 실험 결과차이를 비교하였다. 분석결과, Faster-RCNN의 mAP는 71%이고 Tiny-YOLO-V2의 mAP는 33%로 측정되었으며, 이는 1stage Detection인 YOLO계열 알고리즘보다 2Stage Detection인 Faster-RCNN 계열의 알고리즘이 도로노면의 균열을 확인하고 분리하는데 더 좋은 성능을 보인다는 것을 확인하였다. 향후, 드론과 인공지능형 균열검지시스템을 이용한 도로자산관리체계(Infrastructure Asset Management) 구축방안 마련을 통해 효율적이고 경제적인 도로 유지관리 의사결정 지원 시스템 구축 및 운영 환경을 조성할 수 있을 것이라 판단된다.

Keywords

References

  1. Choi S. H., Do M. S., Yu S. H. and Jo C. S.(2018), "Determination of Visual Based Asphalt Pavement Crack Condition Using Deep Learning," International Journal of Highway Engineering, vol. 20, no. 5, pp.75-83. https://doi.org/10.7855/IJHE.2018.20.5.075
  2. Chong Y. J., Choi H. W. and Park H. C.(2018), "Accurate Spatial Information Mapping System Using MMS LiDAR Data," Journal of the Korean Association of Geographic Information Studies, vol. 21, no. 1, pp.1-11. https://doi.org/10.11108/KAGIS.2018.21.1.001
  3. Dadrasjavan F., Zarrinpanjeh N. and Ameri A.(2019), "Automatic Crack Detection of Road Pavement Based on Aerial UAV Imagery," Preprints 2019, 2019070009(doi: 10.20944/preprints201907.0009.v1).
  4. Gopalakrishnan K., Gholami H., Vidyadharan A., Choudhary A. and Agrawa A.(2019), "Crack damage detection in unmanned aerial vehicle images of civil infrastructure using pre-trained deep learning model," International Journal for Traffic and Transport Engineering, vol. 8, no. 1, pp.1-14. https://doi.org/10.7708/ijtte.2018.8(1).01
  5. Kim J. H., Kim Y. M., Baik N. C. and Won J. M.(2013), "A Development of Stereo Camera based on Mobile Road Surface Condition Detection System," International Journal of Highway Engineering, vol. 15, no. 5, pp.177-185. https://doi.org/10.7855/IJHE.2013.15.5.177
  6. Maeda H., Sekimoto Y., Seto T., Kashiyama T. and Omata H.(2018), Road Damage Detection Using Deep Neural Networks with Images Captured Through a Smartphone, arvi preprint arxiv:1801.09454. https://doi.org/10.1111/mice.12387
  7. Ministry of Land, Infrastucture and Transport(MOLIT)(2019), Yearbook of Road Statistics.
  8. Song H. K., Kang B. J., Lee S. H. and Choi Y. S.(2012), "A Study for Removing Road Shields from Mobile Mapping System of the Laser Data using RTF Filtering Techniques," The Korea Society For Geospatial Information System, vol. 20, no. 1, pp.3-12.
  9. Zhang A., Wang K. C., Li B., Yang E., Dai X., Peng Y., Fei Y., Liu Y., Li J. Q. and Chen C.(2017), "Automated pixel‐level pavement crack detection on 3d asphalt surfaces using a deep learning network," Computer-Aided Civil and Infrastructure Engineering, vol. 32, no. 10, pp.805-819. https://doi.org/10.1111/mice.12297

Cited by

  1. Automatic Classification of Drone Images Using Deep Learning and SVM with Multiple Grid Sizes vol.38, pp.5, 2019, https://doi.org/10.7848/ksgpc.2020.38.5.407
  2. 도로 자산관리를 위한 상태 모니터링 및 경제성 분석 : 세종시를 중심으로 vol.20, pp.4, 2019, https://doi.org/10.12815/kits.2021.20.4.71
  3. Challenges of Data Refining Process during the Artificial Intelligence Development Projects in the Architecture, Engineering and Construction Industry vol.11, pp.22, 2019, https://doi.org/10.3390/app112210919