Two-stage convolutional neural network for road crack detection and segmentation
作者：
Highlights：
• A new two-stage architecture based on Convolutional Neural Networks.
• Take advantages of detection at the sample level and segmentation at the pixel level.
• The two-stage model effective for noisy, low-resolution images, and imbalanced data.
• Experiments and results for several crack road datasets.
• A new dataset of challenging crack road will be made available to the researchers.
摘要
•A new two-stage architecture based on Convolutional Neural Networks.•Take advantages of detection at the sample level and segmentation at the pixel level.•The two-stage model effective for noisy, low-resolution images, and imbalanced data.•Experiments and results for several crack road datasets.•A new dataset of challenging crack road will be made available to the researchers.
论文关键词：Convolutional Neural Networks,Deep learning,Crack detection,Crack segmentation,Crack condition survey
论文评审过程：Received 8 December 2020, Revised 2 August 2021, Accepted 2 August 2021, Available online 9 August 2021, Version of Record 18 August 2021.
论文官网地址：https://doi.org/10.1016/j.eswa.2021.115718
