The Classification of Asphalt Pavement Crack Images Based on Beamlet Transform

Hassan Idow Mohamed; Mustafa Alas

doi:10.55549/epstem.1411085

Conference Paper

Year 2023, Volume: 26, 532 - 540, 30.12.2023

Hassan Idow Mohamed Mustafa Alas

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, Volume: 26, 532 - 540, 30.12.2023

Hassan Idow Mohamed Mustafa Alas

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.

Keywords

Pavement crack detection , Crack classification , Longitudinal crack , Alligator crack , Block crack , Transverse crack , Beamlet transform

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.

There are 3 citations in total.

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 Volume: 26

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