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Year 2023, , 532 - 540, 30.12.2023
https://doi.org/10.55549/epstem.1411085

Abstract

References

  • Alayat, A. B., & Omar, H. A. (2023). Pavement surface distress detection using digital image processing. Techniques, 35(1), 247-256.
  • Deng, L., Zhang, A., Guo, J., & Liu, Y. (2023). An integrated method for road crack segmentation and surface feature quantification under complex backgrounds.Remote Sensing, 15(6).
  • Du, Y., Pan, N., Xu, Z., Deng, F., Shen, Y., & Kang, H. (2020). Pavement distress detection and classification based on YOLO network. International Journal of Pavement Engineering, 22(1), 1–14.

The Classification of Asphalt Pavement Crack Images Based on Beamlet Transform

Year 2023, , 532 - 540, 30.12.2023
https://doi.org/10.55549/epstem.1411085

Abstract

Pavement cracking is a common road infrastructure issue which significantly affects road performance, safety and longevity. This article employed a Beamlet Transform algorithm to detect and classify different types of flexible asphalt concrete pavement cracks. Additionally, a dedicated crack segmentation network was employed for precise segmentation of pavement crack. This approach incorporates advancements that has improve precision in crack classification and segmentation. Based on the results of the beamlet transform, significant improvements in the gray scale representation of crack, enhanced crack detection, reduced noise in crack images and a more precise measurement of cracks length were achieved. Computations were performed to determine the length of linear cracks and the area of block cracks. A total of 1000 pavement images were used for training and testing the accuracy of asphalt pavement crack detection and classification models. The research results showed that block cracking, alligator cracking, transverse cracking, and longitudinal cracking can all be recognized with a remarkable accuracy. Alligator cracks and block cracks achieved detection rates more than 90%, while detection rates for the longitudinal and transverse cracks reached more than 95% accuracy.

References

  • Alayat, A. B., & Omar, H. A. (2023). Pavement surface distress detection using digital image processing. Techniques, 35(1), 247-256.
  • Deng, L., Zhang, A., Guo, J., & Liu, Y. (2023). An integrated method for road crack segmentation and surface feature quantification under complex backgrounds.Remote Sensing, 15(6).
  • Du, Y., Pan, N., Xu, Z., Deng, F., Shen, Y., & Kang, H. (2020). Pavement distress detection and classification based on YOLO network. International Journal of Pavement Engineering, 22(1), 1–14.
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Details

Primary Language English
Subjects Environmental and Sustainable Processes
Journal Section Articles
Authors

Hassan Idow Mohamed

Mustafa Alas

Early Pub Date December 28, 2023
Publication Date December 30, 2023
Published in Issue Year 2023

Cite

APA Mohamed, H. I., & Alas, M. (2023). The Classification of Asphalt Pavement Crack Images Based on Beamlet Transform. The Eurasia Proceedings of Science Technology Engineering and Mathematics, 26, 532-540. https://doi.org/10.55549/epstem.1411085