Design of an intelligent detection method for tunnel lining defects based on deep learning
CHEN Mao
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.
Keywords：deep learningvisual inspectiontunnel constructionlining defects
Publication Date：2025-03-25
Pages：3( 128-130 )