In view of the low contrast between the edge of concrete cracks and the background, this paper proposes a R-Unet target detection framework combined with edge detection operator. Firstly, the Roberts operator is used to extract the target edge feature (3-band), which is fused with the original data to form a 6-band image as input data. Secondly, based on the Unet model, the residual module is introduced into the encoding path to reduce the network overfitting, and the attention module is added in the decoding path to enhance the learning ability of the pixels in the crack area and improve the accuracy of concrete crack extraction; a combination of the cross entropy loss function and Dice loss function is used for evaluation. The results of experiments on the self-made concrete crack datasets show that R-Unet target detection framework is significantly better than the original Unet model and other relevant models. The overall extraction accuracy reached 95.75% with the recall rate of 94.33%, and the MIou of 90.67%. The R-Unet target detection framework proposed can greatly reduce the crack edge false detection rate and improve the target detection accuracy.
Multi-band Feature Images Concrete Crack Segmentation Framework Using Deep Learning
KSCE J Civ Eng
KSCE Journal of Civil Engineering ; 28 , 9 ; 3902-3912
2024-09-01
11 pages
Article (Journal)
Electronic Resource
English
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