Analysis of segmentation performance of U-shape neural network algorithm by multi-type encoders for pavement cracks

Current deep convolutional neural networks have achieved suitable applications in the field of crack segmentation. Among them, the decoding-encoding structure network has achieved good results by the long-connected structure for fusing multi-scale features. However, the analysis of the impact of different types of operators in the encoder on the crack segmentation performance needs to be supplemented. This paper uses asymmetric convolution, depth wise separable convolution, and dilated convolution to replace the conventional convolution in the U-shape network, respectively. Moreover, visualization methods are used to explain the extraction characteristics of crack features by different types of operators. The results show that the HDC rule-compliant dilate convolution in the encoding phase of the U-shape network is optimal for the extraction of the crack segmentation, with the MIOU, Recall, and F1 Score improving by 0.0175, 0.0183, and 0.0127 compared to the baseline model consisting of conventional convolution, respectively.