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September 5, 2025Measurement Science and Technology

Automatic Quality Assessment of Highway Tunnel Linings: A Deep Learning Framework Enhanced by Data Augmentation Techniques

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Authors

MJM. JiangZDZhenzhou DingSWShunguo Wang

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Overview

Experimental results show YOLOv8 improves defect detection accuracy in highway tunnel linings, suggesting enhanced safety implications.

Key Points

  • YOLOv8-LQD improved detection accuracy by 7.4%, highlighting its effectiveness over the original model.
  • The framework integrates data augmentation techniques to generate synthetic images and balance datasets seamlessly.
  • Results indicate YOLOv8-LQD significantly reduces data collection costs while ensuring rapid inference times.
  • This approach offers a scalable solution for automated inspection, enhancing infrastructure maintenance and safety.

Cite This Study

Jiang et al. (2025) studied this question.

synapsesocial.com/papers/68bb3ee82b87ece8dc95704ehttps://doi.org/10.1088/1361-6501/ae0062
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1A GPR Imagery-Based Real-Time Algorithm for Tunnel Lining Void Identification Using Improved YOLOv82025 · 2 citations
  2. 2Efficient Detection of Apparent Defects in Subway Tunnel Linings Based on Deep Learning Methods2024 · 7 citations
  3. 3Intelligent Tunnel Lining Defect Detection: Advances in Image Acquisition and Data-Driven Techniques2025 · 3 citations
  4. 4Attention-Enhanced Dual-Stage Deep Learning for Automated Tunnel Crack Detection and Quantification2026 · 6 citations
  5. 5From Simulation to Reality: GAN-Based Transformation of Pavement Defect Images for YOLO Detection2026 · 1 citations