Structural Health Monitoring Based on Deep Learning and Image Processing

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Electronic Multimedia".

Deadline for manuscript submissions: 17 February 2026 | Viewed by 4

Special Issue Editors


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Guest Editor
School of Mechanical and Aerospace Engineering, Nanyang Technological University, Singapore 639798, Singapore
Interests: structural health monitoring; piezoceramic sensor; ultrasonic; imaging

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Guest Editor
Department of Civil and Environmental Engineering, The Hong Kong Polytechnic University, Hong Kong 999077, China
Interests: structural health monitoring; deep learning; digital–physical fusion; structural resilience

Special Issue Information

Dear Colleagues,

Structural health monitoring (SHM) is a vital technology for ensuring the safety, functionality, and longevity of critical components and systems across a wide spectrum of engineering domains. With recent advances in artificial intelligence, particularly deep learning and image processing, SHM has entered a new era of intelligent, data-driven assessment capable of detecting subtle defects and predicting failures in real time.

This Special Issue aims to present cutting-edge research on SHM methods that integrate deep learning algorithms and image-based techniques across diverse engineering disciplines. We welcome high-quality contributions that explore novel methodologies, practical implementations, and theoretical insights into SHM applications in civil, mechanical, aerospace, marine/offshore, power, and industrial engineering.

Topics of interest include, but are not limited to, the following:

  • Image- and video-based defect detection (e.g., cracks, corrosion, and delamination);
  • Deep learning for damage localization, classification, and prognosis;
  • Computer vision and 3D reconstruction for surface and volumetric monitoring;
  • Multi-modal sensor fusion combining visual, acoustic, and vibration data;
  • UAV and robotic visual inspection systems powered by AI;
  • Transfer learning, domain adaptation, and lightweight models for real-time SHM;
  • Case studies and field applications in complex operational environments.

Dr. Ziqian Yang
Dr. Qingsong Xiong
Guest Editors

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Keywords

  • structural health monitoring
  • deep learning
  • image processing
  • signal processing
  • diagnosis

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