Application of Computer Vision and Deep Learning in Construction Engineering
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 January 2026 | Viewed by 391
Special Issue Editors
Interests: computer vision; artificial intelligence; deep learning; object detection; semantic segmenation; instance segmentation
Interests: construction engineering; automation and control engineering; logistics and supply chain management; technology innovation
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Construction engineering is increasingly benefiting from advancements in computer vision and deep learning technologies, transforming various aspects, such as safety management, quality control, productivity monitoring, and asset management. This Special Issue focuses on innovative research and practical applications leveraging computer vision and deep learning techniques within the domain of construction engineering. We encourage submissions highlighting novel methods, theoretical advancements, experimental studies, and comprehensive reviews addressing challenges and opportunities for integrating these technologies into construction practices. Areas of particular interest include, but are not limited to, automated detection and classification of construction defects, site safety monitoring through image and video analytics, 3D reconstruction and progress monitoring using UAV-based photogrammetry, digital twin creation and management, real-time personnel and equipment tracking, and advanced predictive analytics for project management. Submissions should emphasize both technical developments and practical implications to enhance efficiency, safety, and quality outcomes in construction engineering.
Dr. Seunghyeon Wang
Prof. Sungkon Moon
Guest Editors
Manuscript Submission Information
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Keywords
- computer vision
- deep learning
- construction engineering
- safety monitoring
- defect detection
- photogrammetry
- digital twin
- predictive analytics
- UAV
- image processing
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