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

Pothole Detection Algorithm Based on Saliency Map for Improving Detection Performance

포트홀 탐지 정확도 향상을 위한 Saliency Map 기반 포트홀 탐지 알고리즘

  • 조영태 (한국건설기술연구원 도로연구소) ;
  • 류승기 (한국건설기술연구원 도로연구소)
  • Received : 2016.05.27
  • Accepted : 2016.07.08
  • Published : 2016.08.31
Download PDF
⟨ Previous Next ⟩

Abstract

Potholes have caused diverse problems such as wheel damage and car accident. A pothole detection technology is the most important to provide efficient pothole maintenance. The previous pothole detections have been performed by manual reporting methods. Thus, the problems caused by potholes have not been solved previously. Recently, many pothole detection systems based on video cameras have been studied, which can be implemented at low costs. In this paper, we propose a new pothole detection algorithm based on saliency map information in order to improve our previously developed algorithm. Our previous algorithm shows wrong detection with complicated situations such as the potholes overlapping with shades and similar surface textures with normal road surfaces. To address the problems, the proposed algorithm extracts more accurate pothole regions using the saliency map information, which consists of candidate extraction and decision. The experimental results show that the proposed algorithm shows better performance than our previous algorithm.

포트홀은 차량파손과 교통사고 유발 등의 사회문제를 유발시키고 있다. 포트홀을 효율적으로 관리하기 위해서는 빠르게 포트홀을 찾아내는 기술이 가장 중요하다. 기존의 포트홀 탐지 기법은 민원에 의한 수동식 신고방식을 사용하고 있어, 포트홀로 인해 발생하는 문제를 사전에 예방하지 못하고 있다. 최근 포트홀을 저비용으로 빠르게 탐지하기 위하여 영상 카메라를 이용한 연구가 많이 진행되고 있다. 본 논문에서는 사전에 연구되었던 포트홀 탐지 알고리즘의 탐지정확도를 개선하기 위한 Saliency Map 기반의 알고리즘을 제안한다. 기존 알고리즘은 포트홀이 그림자와 겹쳐있거나 포트홀의 내부 모양이 주변 도로노면과 비슷한 형태를 가지는 등의 복잡한 환경에서 포트홀을 탐지하지 못하는 문제를 가지고 있다. 이러한 문제를 해결하기 위하여 제안하는 알고리즘은 Saliency Map 알고리즘을 이용하여 보다 정확한 포트홀 후보 영역을 찾는다. 제안 알고리즘은 포트홀 후보영역 추출부와 결정부로 구성되며, 실험을 통하여 기존 알고리즘보다 더 높은 탐지 정확도를 가짐을 보인다.

Keywords

References

  1. Seoul city, http://english.seoul.go.kr, 2015.09.01.
  2. Ryu S. K., Kim T. H. and Kim Y. R.(2015), "Feature-Based Pothole Detection in Two-Dimensional Images," Transportation Research Record, no. 2528, pp. 9-17.
  3. Jo Y. T., Ryu S. K. and Kim Y. R.(2016), "Pothole Detection Based on the Features of Intensity and Motion," 2016 TRB Annual Meeting, Washington DC, USA.
  4. Jo Y. T. and Ryu S. K.(2015), "Pothole Detection System Using a Black-box Camera," Sensors, vol. 15, pp.29316-29331. https://doi.org/10.3390/s151129316
  5. Ghose A., Biswas P., Bhaumik C., Sharma M., Pal A. and Jha A.(2012), "Road condition monitoring and alert application : Using invehicle Smartphone as Internet-connected sensor," In Proceedings of 2012 IEEE International Conference on Pervasive Computing and Communications Workshops, Lugano, pp.489-491.
  6. Raj S., Jain A. and Misra R.(2013), "Smartphone sensing for large data set collection of potholes," In Proceedings of MobiSys 2013, Taiwan, pp.517-518.
  7. Li Q., Yao M., Yao X. and Xu B.(2009), "A real-time 3D scanning system for pavement distortion inspection," Measurement Science and Technology, vol. 21, pp.15702-15709.
  8. Yu X. and Salari E.(2011), "Pavement pothole detection and severity measurement using laser imaging," In Proceedings of IEEE International Conference on Electro/Information Technology, Mankato, pp.1-5.
  9. Koch C. and Brilakis I.(2011), "Pothole detection in asphalt pavement images," Advanced Engineering Informatics, vol. 25, pp.507-515. https://doi.org/10.1016/j.aei.2011.01.002
  10. Buza E., Omanovic S. and Huseinnovic A.(2013), "Pothole detection with image processing and spectral clustering," In Proceedings of the 2nd International Conference on Information Technology and Computer Networks, Turkey, pp.48-53.
  11. Jog G. M., Koch C., Golparvar-Fard M. and Brilakis I.(2012), "Pothole properties measurement through visual 2D recognition and 3D reconstruction," In Proceedings of the ASCE International Conference on Computing in Civil Engineering, Florida, pp.553-560.
  12. Land Rover, http://www.landrover.com/experiences/news/pothole-detection.html, 2015.10.05.
  13. Mobileye, http://www.siliconbeat.com/2014/07/28/tesla-watchers-eye-mobileyes-500-millionipo/, 2015.10.05.
  14. Tsotsos J. K., Culhane S. M., Wai W. Y. K., Lai Y., Davis N. and Nuflo F.(1995), "Modeling visual attention via selective tuning," Artificial Intelligence, vol. 78, pp.507-545. https://doi.org/10.1016/0004-3702(95)00025-9
  15. Olshausen B. A., Anderson C. H. and Van Essen D. C.(1993), "A Neurobiological Model of Visual Attension and Invariant Pattern Recognition Based on Dynamic Routing of Information," The Journal of Neuroscience, vol. 13, no. 11, pp.4700-4719. https://doi.org/10.1523/JNEUROSCI.13-11-04700.1993
  16. Itti L., Koch C. and Niebur E.(1998), "A Model of Saliency-based Visual Attention for Rapid Scene Analysis," IEEE Transactions on Pattern Analysis and Mahcine Intelligence, vol. 20, no. 11, pp.1254-1259. https://doi.org/10.1109/34.730558
  17. Ma Y. F. and Zhang H. J,(2003), "Contrastbased image attention analysis by using fuzzy growing," In Proceedings of the Eleventh ACM International Conference on Multimedia, pp.374-381.
  18. Hu Y., Xie X., Ma W. Y., Chia L. T. and Rajan D.(2004), "Salient region detection using weighted feature maps based on the human visual attention model," Springer Lecture Notes in Computer Science, vol. 3332, no. 2, pp.993-1000.
  19. Achanta R., Hemami S., Estrada F. and Susstrunk S.(2009), "Frequency-tuned Salient Region Detection," IEEE International Conference on Computer Vision and Pattern Recognition, pp.1597-1604.
  20. Weken D. V. D., Nachtegael M. and Kerre E.(2004), "Some New Similarity Measures for Histograms," In Proceedings of the Fourth Indian Conference on Computer Vision, Graphics & Image Processing (ICVGIP), Kolkata, India, pp.16-18.
  21. Otsu N.(1975), "A threshold selection method from gray-level histograms," IEEE Transactions on Systems, Man, and Cybernetics, vol. SMC-9, no. 1, pp.62-66.
  22. Adhikari R.S., Moselhi O. and Bagchi A.(2012), "Image-based retrieval of concrete crack properties," Journal of the International Society for Geotechnology, vol. 11, no. 2, pp.315-321.

Cited by

  1. Development of Rapid Setting Material Containing Phosphate for Emergency Repair of Potholes vol.19, pp.7, 2016, https://doi.org/10.9798/kosham.2019.19.7.47
  2. 도로 노면 파손 인식을 위한 Multi-scale 학습 방식의 암호화 형식 의미론적 분할 알고리즘 vol.19, pp.2, 2020, https://doi.org/10.12815/kits.2020.19.2.89
  3. Pothole Classification Model Using Edge Detection in Road Image vol.10, pp.19, 2020, https://doi.org/10.3390/app10196662