@article{ref1,
title="Defect detection of subway tunnels using advanced U-Net network",
journal="Sensors (Basel)",
year="2022",
author="Wang, An and Togo, Ren and Ogawa, Takahiro and Haseyama, Miki",
volume="22",
number="6",
pages="e2330-e2330",
abstract="In this paper, we present a novel defect detection model based on an improved U-Net architecture. As a semantic segmentation task, the defect detection task has the problems of background-foreground imbalance, multi-scale targets, and feature similarity between the background and defects in the real-world data. Conventionally, general convolutional neural network (CNN)-based networks mainly focus on natural image tasks, which are insensitive to the problems in our task. The proposed method has a network design for multi-scale segmentation based on the U-Net architecture including an atrous spatial pyramid pooling (ASPP) module and an inception module, and can detect various types of defects compared to conventional simple CNN-based methods. Through the experiments using a real-world subway tunnel image dataset, the proposed method showed a better performance than that of general semantic segmentation including state-of-the-art methods. Additionally, we showed that our method can achieve excellent detection balance among multi-scale defects.<p /> <p>Language: en</p>",
language="en",
issn="1424-8220",
doi="10.3390/s22062330",
url="http://dx.doi.org/10.3390/s22062330"
}