Journal
CONSTRUCTION AND BUILDING MATERIALS
Volume 358, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.conbuildmat.2022.129117
Keywords
Pavement crack; Deep learning; Weakly supervised learning; Automatic recognition; Instance segmentation
Categories
Funding
- National Key R&D Program of China
- [2018YFB1600100]
- [2018YFB1600105]
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This paper presents efficient and cost-effective methods to identify pavement crack distress using deep learning and the weakly supervised instance segmentation framework, which reduces manual marking and achieves highly accurate recognition of crack distress.
This paper presents efficient and cost-effective methods to identify pavement crack distress and thereby substantially increase pavement strength. Detecting the origin of this distress is the key to restoring pavement performance. To do that, a deep learning method is used to detect cracks based on the weakly supervised instance segmentation (WSIS) framework. A bounding box-level crack image data is preprocessed. Pseudo labels are generated by a region growing algorithm and a GrabCut algorithm. Another important contribution is a new dynamically balanced binary cross-entropy loss function. Results show that the WSIS framework reduces manual marking and has a high recognition accuracy of crack distress.
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