南京航空航天大学学报（英文版）2025，Vol.42Issue(6)：749-766,18.DOI:10.16356/j.1005-1120.2025.06.004
基于多尺度卷积神经网络的路面裂缝提取
Pavement Crack Extraction Based on Multi-scale Convolutional Neural Network
摘要
Abstract
Cracks represent a significant hazard to pavement integrity,making their efficient and automated extraction essential for effective road health monitoring and maintenance.In response to this challenge,we propose a crack automatic extraction network model that integrates multi-scale image features,thereby enhancing the model's capability to capture crack characteristics and adaptation to complex scenarios.This model is based on the ResUNet architecture,makes modification to the convolutional layer of the model,proposes to construct multiple branches utilizing different convolution kernel sizes,and adds a atrous spatial pyramid pooling module within the intermediate layers.In this paper,comparative experiments on the performance of the basic model,ablation experiments,comparative experiments before and after data augmentation,and generalization verification experiments are conducted.Comparative experimental results indicate that the improved model exhibits superior detail processing capability at crack edges.The overall performance of the model,as measured by the F1-score,reaches 71.03%,reflecting a 2.1%improvement over the conventional ResUNet.关键词
道路工程/神经网络/多尺度卷积/路面裂缝Key words
road engineering/neural networks/multi-scale convolution/pavement cracks分类
信息技术与安全科学引用本文复制引用
詹必恒,宋翔宇,程建蕊,乔盘,王腾飞..基于多尺度卷积神经网络的路面裂缝提取[J].南京航空航天大学学报（英文版）,2025,42(6):749-766,18.基金项目
This work was supported in part by the National Natural Science Foundation of China(No.42401166),the Open Fund of Key Laboratory of Polar En-vironment Monitoring and Public Governance,Ministry of Education(No.202405),and the Key Research and Devel-opment Program of Hebei Province(No.23375405D). （No.42401166）
