Automated Pavement Distress Detection Using Image Processing Techniques
Pavement crack and pothole identification are important tasks in transportation maintenance and road safety. This study offers a novel technique for automatic asphalt pavement crack and pothole detection which is based on image processing. Different types of cracks (transverse, longitudinal, alligator-type, and potholes) can be identified with such techniques. The goal of this research is to evaluate road surface damage by extracting cracks and potholes, categorizing them from images and videos, and comparing the manual and the automated methods. The proposed method was tested on 50 images. The results obtained from image processing showed that the proposed method can detect cracks and potholes and identify their severity levels with a medium validity of 76%. There are two kinds of methods, manual and automated, for distress evaluation that are used to assess pavement condition. A committee of three expert engineers in the maintenance department of the Mayoralty of Baghdad did the manual assessment of a highway in Baghdad city by using a Pavement Condition Index (PCI). The automated method was assessed by processing the videos of the road. By comparing the automated with the manual method, the accuracy percentage for this case study was 88.44%. The suggested method proved to be an encouraging solution for identifying cracks and potholes in asphalt pavements and sorting their severity. This technique can replace manual road damage assessment.
Keywords:pavement distress, AEOP, python code, image processing
I. J. Mrema and M. A. Dida, "A Survey of Road Accident Reporting and Driver’s Behavior Awareness Systems: The Case of Tanzania," Engineering, Technology & Applied Science Research, vol. 10, no. 4, pp. 6009–6015, Aug. 2020.
M. Touahmia, "Identification of Risk Factors Influencing Road Traffic Accidents," Engineering, Technology & Applied Science Research, vol. 8, no. 1, pp. 2417–2421, Feb. 2018.
A. Detho, S. R. Samo, K. C. Mukwana, K. A. Samo, and A. A. Siyal, "Evaluation of Road Traffic Accidents (RTAs) on Hyderabad Karachi M-9 Motorway Section," Engineering, Technology & Applied Science Research, vol. 8, no. 3, pp. 2875–2878, Jun. 2018.
D. Akhila and V. Preeja, "A Novel Technique for Automatic Road Distress Detection and Analysis," International Journal of Computer Applications, vol. 101, no. 10, pp. 18–23, 2014.
C. Chen et al., "Automatic Pavement Crack Detection Based on Image Recognition," in International Conference on Smart Infrastructure and Construction, Cambridge, UK, Jul. 2019, pp. 361–369.
T. S. Tran, V. P. Tran, H. J. Lee, J. M. Flores, and V. P. Le, "A two-step sequential automated crack detection and severity classification process for asphalt pavements," International Journal of Pavement Engineering, Oct. 2020.
H. Oliveira and P. L. Correia, "Automatic road crack segmentation using entropy and image dynamic thresholding," in 17th European Signal Processing Conference, Glasgow, UK, Aug. 2009, pp. 622–626.
Q. Zou, Y. Cao, Q. Li, Q. Mao, and S. Wang, "CrackTree: Automatic crack detection from pavement images," Pattern Recognition Letters, vol. 33, no. 3, pp. 227–238, Feb. 2012.
G. Sollazzo, K. C. P. Wang, G. Bosurgi, and J. Q. Li, "Hybrid Procedure for Automated Detection of Cracking with 3D Pavement Data," Journal of Computing in Civil Engineering, vol. 30, no. 6, Nov. 2016, Art. no. 04016032.
S. Li, Y. Cao, and H. Cai, "Automatic Pavement-Crack Detection and Segmentation Based on Steerable Matched Filtering and an Active Contour Model," Journal of Computing in Civil Engineering, vol. 31, no. 5, Sep. 2017, Art. no. 04017045.
B. Li, K. C. P. Wang, A. Zhang, Y. Fei, and G. Sollazzo, "Automatic Segmentation and Enhancement of Pavement Cracks Based on 3D Pavement Images," Journal of Advanced Transportation, vol. 2019, Feb. 2019, Art. no. e1813763.
Y. Huang and B. Xu, "Automatic inspection of pavement cracking distress," Journal of Electronic Imaging, vol. 15, no. 1, Jan. 2006, Art. no. 013017.
M. Mustaffar, T. C. Ling, and O. C. Puan, "Automated pavement imaging program (APIP) for pavement cracks classification and quantification-a photogrammetric approach," The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, vol. 37, no. B4, pp. 367–372, 2008.
F. Liu, G. Xu, Y. Yang, X. Niu, and Y. Pan, "Novel Approach to Pavement Cracking Automatic Detection Based on Segment Extending," in International Symposium on Knowledge Acquisition and Modeling, Wuhan, China, Dec. 2008, pp. 610–614.
T. S. Nguyen, M. Avila, and S. Begot, "Automatic detection and classification of defect on road pavement using anisotropy measure," in 17th European Signal Processing Conference, Glasgow, UK, Aug. 2009, pp. 617–621.
G. Pascale and A. Lolli, "Crack assessment in marble sculptures using ultrasonic measurements: Laboratory tests and application on the statue of David by Michelangelo," Journal of Cultural Heritage, vol. 16, no. 6, pp. 813–821, Nov. 2015.
Y.-C. Tsai, V. Kaul, and R. M. Mersereau, "Critical Assessment of Pavement Distress Segmentation Methods," Journal of Transportation Engineering, vol. 136, no. 1, pp. 11–19, Jan. 2010.
J. Jiang, "Crack Enhancement Algorithm Based on Improved EM," The Journal of Information and Computational Science, vol. 12, pp. 1037–1043, 2015.
M. Gavilan et al., "Adaptive Road Crack Detection System by Pavement Classification," Sensors, vol. 11, no. 10, pp. 9628–9657, Oct. 2011.
P. Subirats, J. Dumoulin, V. Legeay, and D. Barba, "Automation of Pavement Surface Crack Detection using the Continuous Wavelet Transform," in International Conference on Image Processing, Atlanta, GA, USA, Oct. 2006, pp. 3037–3040.
C. Koch and I. Brilakis, "Pothole detection in asphalt pavement images," Advanced Engineering Informatics, vol. 25, no. 3, pp. 507–515, Aug. 2011.
R. Chandel and G. Gupta, "Image Filtering Algorithms and Techniques: A Review," International Journal of Advanced Research in Computer Science and Software Engineering, vol. 3, no. 10, pp. 198–202, 2013.
N. M. Zaitoun and M. J. Aqel, "Survey on Image Segmentation Techniques," Procedia Computer Science, vol. 65, pp. 797–806, Jan. 2015.
G. Jie and L. Ning, "An Improved Adaptive Threshold Canny Edge Detection Algorithm," in International Conference on Computer Science and Electronics Engineering, Hangzhou, China, Mar. 2012, vol. 1, pp. 164–168.
R. R. Chavan, S. A. Chavan, G. D. Dokhe, M. B. Wagh, and A. S. Vaidya, "Quality Control of PCB using Image Processing," International Journal of Computer Applications, vol. 141, no. 5, pp. 28–32, 2016.
R. S. Choras, "Image Feature Extraction Techniques and Their Applications for CBIR and Biometrics Systems," International journal of biology and biomedical engineering, vol. 1, no. 1, pp. 6–16, 2007.
ASTM D6433-07(2007), Standard practice for roads and parking lots pavement condition index surveys. West Conshohocken, PA, USA: ASTM International, 2007.
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