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 57
Special Issue Editors
Interests: structural health monitoring; piezoceramic sensor; ultrasonic; imaging
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
Manuscript Submission Information
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Keywords
- structural health monitoring
- deep learning
- image processing
- signal processing
- diagnosis
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