期刊
AUTOMATION IN CONSTRUCTION
卷 129, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.autcon.2021.103788
关键词
Pavement distress detection; Stereo vision; Deep learning; U-net; Depthwise separable convolution; Crack and pothole segmentation
资金
- National Natural Science Foundation of China [52078049]
- Fundamental Research Funds for the Central Universities, CHD [300102210302, 300102210118]
- 111 Project of Sustainable Transportation for Urban Agglomeration in Western China [B20035]
An automated pixel-level pavement distress detection framework integrating stereo vision and deep learning is developed in this study, which establishes multi-feature pavement image datasets based on a multi-view stereo imaging system and proposes a modified U-net deep learning architecture introducing depthwise separable convolution for efficient crack and pothole segmentation. The results show that the 3D pavement image achieves millimeter-level accuracy, and the enhanced 3D crack segmentation model outperforms other models in terms of accuracy and speed, enabling high-precision automated pothole volume measurement.
Automated pavement distress detection based on 2D images is facing various challenges. To efficiently complete the crack and pothole segmentation in a practical environment, an automated pixel-level pavement distress detection framework integrating stereo vision and deep learning is developed in this study. Based on the multi-view stereo imaging system, multi-feature pavement image datasets containing color images, depth images and color-depth overlapped images are established, providing a new perspective for deep learning. To alleviate computational burden, a modified U-net deep learning architecture introducing depthwise separable convolution is proposed for crack and pothole segmentation. These methods are tested in asphalt roads with different cir-cumstances. The results show that the 3D pavement image achieves millimeter-level accuracy. The enhanced 3D crack segmentation model outperforms other models in terms of segmentation accuracy and inference speed. After obtaining the high-resolution pothole segmentation map, the automated pothole volume measurement is realized with high accuracy.
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