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A platform for research: civil engineering, architecture and urbanism
Asphalt Pavement Crack Detection Based on SegNet Network
A novel scheme combining image processing with deep learning is proposed to solve the problems of illumination non-uniformity and impurities in asphalt pavement images. The method first used illumination non-uniformity correction, contrast enhancement, and image denoising methods to highlight the cracks. The SegNet network is then used to achieve effective crack segmentation. Finally, through morphological methods and regional connections, interference is effectively removed, and the final crack skeleton is obtained. The results show that the Mean Intersection over union can reach 70.08%, which is about 3% points higher than that of the method without pre-processing, and achieves a good detection effect.
Asphalt Pavement Crack Detection Based on SegNet Network
A novel scheme combining image processing with deep learning is proposed to solve the problems of illumination non-uniformity and impurities in asphalt pavement images. The method first used illumination non-uniformity correction, contrast enhancement, and image denoising methods to highlight the cracks. The SegNet network is then used to achieve effective crack segmentation. Finally, through morphological methods and regional connections, interference is effectively removed, and the final crack skeleton is obtained. The results show that the Mean Intersection over union can reach 70.08%, which is about 3% points higher than that of the method without pre-processing, and achieves a good detection effect.
Asphalt Pavement Crack Detection Based on SegNet Network
Jun Zhao, M. (author) / Song, Beibei (author) / Fan He, M. (author) / Suina Ma, M. (author) / Fangfang Kong, M. (author)
20th COTA International Conference of Transportation Professionals ; 2020 ; Xi’an, China (Conference Cancelled)
CICTP 2020 ; 1930-1942
2020-12-09
Conference paper
Electronic Resource
English
Vision-Based Crack Detection of Asphalt Pavement Using Deep Convolutional Neural Network
| Springer Verlag | 2021