ArticleSensors (Basel, Switzerland)2022
Defect Detection of Subway Tunnels Using Advanced U-Net Network.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- MFPNet: A Semantic Segmentation Network for Regular Tunnel Point Clouds Based on Multi-Scale Feature Perception.Sensors (Basel, Switzerland) · 2026Article
- AI-based rock strength assessment from tunnel face images using hybrid neural networks.Scientific reports · 2024Article
- MFF-YOLO: An Accurate Model for Detecting Tunnel Defects Based on Multi-Scale Feature Fusion.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
Funding
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.
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Registered trials
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