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• W4281750281 abstract "Crack images collected from civil infrastructures through unmanned aerial vehicles suffer from motion blur and insufficient resolution, which reduces the accuracy of microcrack detection. Therefore, an automatic microcrack detection method based on super-resolution reconstruction (SRR) and semantic segmentation is proposed. Super-resolution (SR) images reconstructed by the proposed deep learning-based SRR model were input into the proposed semantic segmentation network for crack segmentation, and the length and width of cracks were measured through an improved medial axis transform approach. The accuracy of crack segmentation and feature quantification for SR images obtained using the deep learning-based SRR is significantly improved compared with low-resolution fuzzy images. The effects of three parameters on the results were analyzed. Compared with the Bicubic testset, the Intersection-over-Union of the SR testset is improved by 17% when a magnification factor of 4 is adopted. The results show that the proposed method achieves good performance in detecting concrete cracks. • A crack detection method based on deep learning SRR and segmentation is proposed. • SRFBN with the least number of parameters achieves the best results on crack SRR. • The F1-score and IoU obtained from the SR testset are improved by 13% and 17%. • The training set combining natural scene images with crack images performs best. • The effects of training sets and magnification factors on crack SRR are discussed." @default.
• W4281750281 created "2022-06-13" @default.
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• W4281750281 date "2022-08-01" @default.
• W4281750281 modified "2023-09-29" @default.
• W4281750281 title "Crack detection algorithm for concrete structures based on super-resolution reconstruction and segmentation network" @default.
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• W4281750281 doi "https://doi.org/10.1016/j.autcon.2022.104346" @default.
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