摘要
公路路面病害检测作为道路养护管理的关键环节,其检测效率与准确性直接影响养护决策的科学性。本研究构建了融合深度学习算法与边缘计算架构的路面病害智能检测系统,通过卷积神经网络实现病害特征的自动提取与分类。系统采用多尺度特征融合策略,结合注意力机制优化病害区域定位精度。研究成果为公路养护智能化转型提供了技术支撑,推动了预防性养护模式的实践应用。
As a key link in road maintenance management,the detection of road surface diseases directly affects the scientific nature of maintenance decisions in terms of detection efficiency and accuracy.In this study,an intelligent detection system for pavement diseases based on the fusion of deep learning algorithm and edge computing architecture is constructed,and the disease features are automatically extracted and classified through convolutional neural network.The system adopts a multi-scale feature fusion strategy,combined with attention mechanism to optimize the accuracy of disease area localization.The research results provide technical support for the intelligent transformation of highway maintenance and promote the practical application of preventive maintenance mode.
作者
卢浩雨
张鑫
王婵
阴一凡
Haoyu Lu;Xin Zhang;Chan Wang;Yifan Yin(Shaanxi Transportation Electronic Engineering Technology Co.Ltd.,Xi'an,Shaanxi 710000)
出处
《新疆钢铁》
2025年第4期269-271,共3页
Xinjiang Iron and Steel
关键词
深度学习
路面病害
图像识别
边缘计算
预防性养护
deep learning
road surface diseases
image recognition
edge computing
preventive maintenance
