A Computational Method for Real-time Roof Defect Segmentation in Robotic Inspection
Published in Computer‐Aided Civil and Infrastructure Engineering, 2024
This paper introduces RRD-SegNet, a deep learning-based computational method designed for real-time roof defect segmentation in robotic inspection systems. The method enables automated detection and segmentation of roof defects, improving the efficiency and accuracy of construction inspection tasks.
Recommended citation: Zhao, X. and Jebelli, H. (2024). “RRD-SegNet: A Computational Method for Real-time Roof Defect Segmentation in Robotic Inspection.” Computer‐Aided Civil and Infrastructure Engineering. (Journal Under Review).
Recommended citation: Zhao, X. and Jebelli, H. (2024). "RRD-SegNet: A Computational Method for Real-time Roof Defect Segmentation in Robotic Inspection." Computer‐Aided Civil and Infrastructure Engineering. (Journal Under Review).