Convolutional Neural Network with Attention Module for Identification of Tunnel Seepage

As tunnel construction proceeds ever more rapidly, the efficiency of seepage detection by engineers with expert knowledge is facing unprecedented challenges. Moreover, it suffers from strong subjectivity. In recent years, deep learning, as an algorithm of machine learning, has achieved state-of-the-art performance in pattern recognition. In this paper, we address such a problem by building convolutional neural networks that operate on conventional graphics processing units. Within the project, the data is obtained by an infrared thermal imager since there exist different characteristics of temperature between the area of seepage and non-seepage. Considering the difficulty of collecting many images, generative adversarial nets and other data augmentation skills are applicable to enlarge data sets. We design several novel architectures where the attention mechanism is plugged into various traditional models, considered as VGG16 network with Attention Module and RestNet34 with Attention Module, and the overall identification accuracy achieved is more than 97%. The codes of this project can be found at https://github.com/Scotter-Qian/cnn.

• Record URL:
  https://doi.org/10.1177/03611981221091774
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/03611981
• Supplemental Notes:
  • The codes of this project can be found at https://github.com/Scotter-Qian/cnn. © National Academy of Sciences: Transportation Research Board 2022.
• Authors:
  • Chen, Qian
  • Xiong, Chuanguo
  • Lv, Weishan
  • Shen, Ben
  • Zeng, Baoshan
  • Li, Jinming
  • Feng, Chenzefang
  • Hu, Zhou
  • Zhu, Fulong
• Publication Date: 2022-11

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 112-123
• Serial:
  • Transportation Research Record: Journal of the Transportation Research Board
  • Volume: 2676
  • Issue Number: 11
  • Publisher: Sage Publications, Incorporated
  • ISSN: 0361-1981
  • EISSN: 2169-4052
  • Serial URL: http://journals.sagepub.com/home/trr

Subject/Index Terms

• TRT Terms: Detection and identification; Machine learning; Neural networks; Seepage; Tunneling; Tunnels
• Subject Areas: Bridges and other structures; Construction; Transportation (General);

Filing Info

• Accession Number: 01847384
• Record Type: Publication
• Files: TRIS, TRB, ATRI
• Created Date: May 26 2022 4:59PM