Improved HRNet applied to segmentation and detection of pavement cracks

doi:10.13474/j.cnki.11-2246.2022.0082

Abstract

Abstract: Aiming at the problems of low accuracy,loss of information and blurred edges in the traditional convolutional neural network for pavement crack segmentation,a pavement crack segmentation algorithm based on the improved HRNet model is proposed.The model is improved on the basis of the original HRNet,the backbone network part uses DUC module instead of bilinear interpolation;downsampling is changed to passthrough layer to replace the original convolution,SE-Block is introduced while performing step-by-step upsampling to re-calibrate the fusion of different feature layers.Comparing with the original HRNet and the other traditional convolutional neural networks U-Net,it can be concluded that the segmentation accuracy of this algorithm is the best on public data and self-made data sets,with F1 score reaching 91.31% and 78.69% respectively,proving that the algorithm can be very good to meet the needs of actual engineering.

Key words: road cracks;HRNet;DUC;passthrough layer;SE-Block;image segmentation

CLC Number:

P208

ZHANG Boshu, ZHANG Zhihua, ZHANG Yang. Improved HRNet applied to segmentation and detection of pavement cracks[J]. Bulletin of Surveying and Mapping, 2022, 0(3): 83-89.

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