Study on the uniformity of asphalt pavement construction based on dilated convolution
ZHANG Yongan
Abstract：The study started with the cement-stabilized crushed stone base,collected data and constructed a dataset,and used Image Labeler software for data annotation and data augmentation.A semantic segmentation model based on the DeepLabV3+network with dilated convolution combined with MobileNetV2 was designed,and the watershed method was further used to improve the model's segmentation results.The results showed that the maximum accuracy of the model was 80.31%,and the minimum was 53.77%.The F-values of the model on aggregate and background images were 0.851 27 and 0.881 27,respectively.After processing with the model and the watershed method,the static moment variation coefficient of the images was mainly concentrated in the range of 0.03 to 0.17.The semantic segmentation model based on dilated convolution designed in this study showed good performance in the detection of the construction uniformity of asphalt pavement and could effectively improve the detection effect.
Keywords：dilated convolutionpavementconstructionuniformitywater stable layer
Publication Date：2025-02-27
Pages：3( 140-142 )