Defect Detection of Subway Tunnels Using Advanced U|Net Network

Defect Detection of Subway Tunnels Using Advanced U-Net Network

By introducing ASPP and inception modules in the U-Net-based network architecture, we improved the capacity of the network for defect detection. The ...

Defect Detection of Subway Tunnels Using Advanced U-Net Network

In this paper, we present a novel defect detection model based on an improved U-Net architecture. As a semantic segmentation task, ...

Examples of subway tunnel images used in this study. (a,b) are ...

12088 × 10000 pixels). from publication: Defect Detection of Subway Tunnels Using Advanced U-Net Network | In this paper, we present a novel defect detection ...

Image-based intelligent detection of typical defects of complex ...

Herein, we present a subway tunnel surface defect detection system that achieves efficient and accurate results. Specifically,. (1) A six-camera area-array ...

Efficient Detection of Apparent Defects in Subway Tunnel Linings ...

Defect Detection of Subway Tunnels Using Advanced U-Net Network. Sensors ... Automatic defect detection of metro tunnel surfaces using a vision-based inspection ...

Automatic defect detection of metro tunnel surfaces using a vision ...

To this end, we propose a novel feature fusion network, based on the Faster RCNN [9], to defect metro tunnel surface defects from the collected massive tunnel ...

Automatic Tunnel Crack Detection Based on U-Net and a ...

This study proposes a deep learning algorithm based on U-Net and a convolutional neural network with alternately updated clique (CliqueNet), called U-CliqueNet ...

Developing technologies for the practical application of deep ...

and Haseyama, M.: Defect detection of subway tunnels using advanced U-Net network, Sensors, Vol. 22, No. 6, pp. 2330, 2022. 10) Maeda, K., Takada, S ...

12 publications - OUCI

Defect Detection of Subway Tunnels Using Advanced U-Net Network. An Wang, Ren Togo, Takahiro Ogawa, Miki Haseyama. In this paper, we present a novel defect ...

A crack detection system of subway tunnel based on image processing

PDF | For the images of crack defects of subway tunnel, traditional image processing algorithms is hardly effective for dealing with ...

A highly efficient tunnel lining crack detection model based on Mini ...

Automatic defect detection of metro tunnel surfaces using a vision-based inspection system. ... U-net: Convolutional networks for ...

Image-based automatic multiple-defect detection of urban utility ...

In-service urban utility tunnels (UUT) suffer from cracks, corrosion, and leakage defects, which rises the chance of major accidents.

Defect detection of subway tunnels using advanced U-net network

Defect detection of subway tunnels using advanced U-net network. An Wang, Ren Togo, Takahiro Ogawa, Miki Haseyama. Sensors.

You can see underground subway stations through tunnels, pretty neat

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A crack detection system of subway tunnel based on image processing

For the images of crack defects of subway tunnel, traditional image processing algorithms is hardly effective for dealing with problems ...

Image segmentation using Vision Transformer for tunnel defect ...

AbstractExisting tunnel detection methods include crack and water‐leakage segmentation networks. However, if the automated detection ...

Ren Togo - Google Scholar

2022. Defect detection of subway tunnels using advanced U-Net network‏. A Wang, R Togo, T Ogawa, M Haseyama‏. Sensors 22 (6), 2330, 2022‏. 20, 2022. Bone ...

Subway Tunnel Crack Identification based on YOLOv5

NB-CNN: Deep learning-based crack detection using convolutional neural network and Naïve Bayes datafusion [J]. IEEE Transactions on ...

Deep Learning with Spatial Constraint for Tunnel Crack Detection

Cracks are the most common defect on the surface of tunnels, which potentially brings threaten to the safety of the tunnel and the running vehicles.

Efficient real-time defect detection for spillway tunnel using deep ...

Then, the lightweight STDD network is developed using separable convolution and asymmetric convolution, and the network is trained and tested on the dataset. To ...