Image-Based Surface Damage Detection
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Sensing and Imaging".
Deadline for manuscript submissions: 31 August 2026 | Viewed by 245
Special Issue Editors
Interests: computer vision; data visualization; deep learning; advanced machine learning; damage detection
Interests: developing scalable preprocessing and large-scale machine learning techniques for big data streams; data mining; natural language processing; information retrieval; application of deep learning in healthcare; distributed computing; mHealth wearable sensor data analytics
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Image-based surface damage detection has become a cornerstone of modern condition monitoring for critical infrastructure and industrial assets. Advances in sensing hardware and embedded computing now allow cameras, depth sensors, thermal imagers, and drone or mobile platforms to capture rich visual evidence of cracks, spalling, corrosion, coating failures, and other surface defects at scale. At the same time, progress in computer vision and deep learning enables automated detection, localization, segmentation, and severity estimation under challenging real-world conditions such as low light, motion blur, weather effects, and domain shift across sites and materials.
For this Special Issue, “Image-Based Surface Damage Detection,” we are seeking contributions that advance image-based algorithms and deployable systems for detecting and characterizing surface defects. We welcome work on detection, localization, segmentation, and severity assessment, including model architectures, learning objectives, and training strategies that improve accuracy and robustness. Submissions may address practical considerations relevant to real deployments, such as handling varied imaging conditions, limited labels, class imbalance, and cross-domain generalization, but such factors are not required. We also encourage research on efficient inference and deployment, including real-time processing, edge/embedded implementations, model compression and acceleration, and system-level evaluation on field data. Additional topics of interest include uncertainty estimation, explainable and human-assisted inspection workflows, and benchmarking methodologies that enable fair comparison and reproducibility. Applications may span transportation and civil infrastructure, industrial inspection and coatings, energy systems, and public safety, with an emphasis on methods that translate imagery into actionable, reliable damage assessments.
This theme fits the scope of Sensors by emphasizing how sensing modalities, measurement quality, and system constraints shape reliable visual inspection, and by advancing sensor-driven methods that translate imagery into actionable, reproducible assessments for monitoring and decision-making.
Dr. Van Vung Pham
Dr. Vibhuti Gupta
Guest Editors
Manuscript Submission Information
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Keywords
- computer vision
- surface damage detection
- crack detection
- defect segmentation
- visual inspection
- structural health monitoring
- multimodal sensing
- UAV/drone imaging
- edge AI
- domain adaptation
- uncertainty estimation
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