Automatic Classification and Segmentation of Tunnel Cracks Based on Deep Learning and Visual Explanations

TL;DR

This paper presents a two-step deep learning approach combining classification and segmentation, enhanced with visual explanations, to accurately assess tunnel crack conditions efficiently.

Contribution

It introduces a novel two-stage method using DenseNet-169 and DeepLabV3+ with visual explanations for improved tunnel crack detection.

Findings

Classification accuracy of 92.23% and FPS of 39.80.

Segmentation IoU of 57.01% and F1 score of 67.44%.

Outperforms existing CNN and Transformer models.

Abstract

Tunnel lining crack is a crucial indicator of tunnels' safety status. Aiming to classify and segment tunnel cracks with enhanced accuracy and efficiency, this study proposes a two-step deep learning-based method. An automatic tunnel image classification model is developed using the DenseNet-169 in the first step. The proposed crack segmentation model in the second step is based on the DeepLabV3+, whose internal logic is evaluated via a score-weighted visual explanation technique. Proposed method combines tunnel image classification and segmentation together, so that the selected images containing cracks from the first step are segmented in the second step to improve the detection accuracy and efficiency. The superior performances of the two-step method are validated by experiments. The results show that the accuracy and frames per second (FPS) of the tunnel crack classification model are 92.23% and 39.80, respectively, which are higher than other convolutional neural networks (CNN) based and Transformer based models. Also, the intersection over union (IoU) and F1 score of the tunnel crack segmentation model are 57.01% and 67.44%, respectively, outperforming other state-of-the-art models. Moreover, the provided visual explanations in this study are conducive to understanding the "black box" of deep learning-based models. The developed two-stage deep learning-based method integrating visual explanations provides a basis for fast and accurate quantitative assessment of tunnel health status.