Abstract
Recent studies have suggested that the fusion of cross-modal information can enhance the performance of deep learning-based segmentation algorithms. In this context, this study evaluates the benefits of RGB-D fusion with regard to damage segmentation in reinforced concrete buildings. The fusion of depth data was observed to enhance the segmentation performance significantly. Additionally, a number of surrogate techniques based on modality hallucination and monocular depth estimation are exploited to eliminate the need for depth sensing at test time without foregoing the benefits of depth fusion. The proposed techniques require depth data only for network training, and at test time, depth features are simulated from the corresponding RGB frames, obliterating the need for real depth perception. The proposed methods are evaluated and are shown to increase the damage segmentation accuracy.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
