A Hybrid Deep Learning Model for Enhanced Structural Damage Detection: Integrating ResNet50, GoogLeNet, and Attention Mechanisms.

Journal: Sensors (Basel, Switzerland)
Published Date: Nov 13, 2024

Abstract

Quick and accurate structural damage detection is essential for maintaining the safety and integrity of infrastructure, especially following natural disasters. Traditional methods of damage assessment, which rely on manual inspections, can be labor-intensive and subject to human error. This paper introduces a hybrid deep learning model that combines the capabilities of ResNet50 and GoogLeNet, further enhanced by a convolutional block attention module (CBAM), proposed to improve both the accuracy and performance in detecting structural damage. For training purposes, a diverse dataset of images depicting both structural damage cases and undamaged cases was used. To further enhance the robustness, data augmentation techniques were also employed. In this research, precision, recall, F1-score, and accuracy were employed to evaluate the effectiveness of the introduced hybrid deep learning model. Our findings indicate that the hybrid deep neural network introduced in this study significantly outperformed standalone architectures such as ResNet50 and GoogLeNet, making it a highly effective solution for applications in disaster response and infrastructure maintenance.

Authors

  • Vikash Singh
    University of California at Los Angeles, Los Angeles, CA, USA.
  • Anuj Baral
    Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Udupi 576104, India.
  • Roshan Kumar
    Department of Electronic and Information Technology, Miami College, Henan University, Kaifeng 475004, China.
  • Sudhakar Tummala
    Department of Radiology, Huzhou Wuxing People's Hospital, Huzhou Wuxing Maternity and Child Health Hospital, Huzhou 313000, China.
  • Mohammad Noori
    Department of Mechanical Engineering, California Polytechnic State University, San Luis Obispo, CA 93407, USA.
  • Swati Varun Yadav
    Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Udupi 576104, India.
  • Shuai Kang
    Hepatology Unit and Department of Infectious Diseases, Nanfang Hospital, Southern Medical University, Guangzhou, 510515, China.
  • Wei Zhao
    Key Laboratory of Synthetic and Biological Colloids, Ministry of Education, Jiangnan University, Wuxi 214122, Jiangsu Province, P. R. China. lxy@jiangnan.edu.cn zhuye@jiangnan.edu.cn.

Keywords

External Resources

Popular Topics
Recent Journals