智能城市2025，Vol.11Issue(3)：128-130,3.DOI:10.19301/j.cnki.zncs.2025.03.039
基于深度学习的隧道衬砌病害智能检测方法设计
Design of an intelligent detection method for tunnel lining defects based on deep learning
陈茂1
作者信息
- 1. 中铁十六局集团第一工程有限公司,北京 101300
- 折叠
摘要
Abstract
In order to enhance the automation level of tunnel lining defect detection and fundamentally improve the quality of tunnel construction,this article proposes an intelligent detection method for tunnel lining defects based on the SegNet deep neural network.This method utilises a mobile detection device that integrates various video capture equipment and possesses mobility to obtain raw images of the tunnel lining,which are then input into the SegNet network.Through the collaborative processing of the encoder,decoder,and classifier,the final output is feature information marked with lining defect identifiers.Combining actual engineering construction cases,the article establishes evaluation metrics for the recognition results of the intelligent detection method and conducts statistical analysis,with results indicating that this method has high accuracy and practicality.The research lays the foundation for promoting the application of deep learning-based computer vision technology in quality detection of engineering construction.关键词
深度学习/视觉检测/隧道建设/衬砌病害Key words
deep learning/visual inspection/tunnel construction/lining defects分类
交通工程引用本文复制引用
陈茂..基于深度学习的隧道衬砌病害智能检测方法设计[J].智能城市,2025,11(3):128-130,3.
