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fusion of color and hallucinated depth features for enhanced multimodal deep learning-based damage segmentation

Earthquake Engineering and Engineering Vibration 2023年第1期22卷 55-68页

作者： Tarutal Ghosh Mondal Mohammad Reza Jahanshahi Civil Architectural and Environmental EngineeringMissouri University of Science and TechnologyRollaMOUSA Lyles School of Civil Engineering Purdue UniversityWest LafayetteINUSA School of Electrical and Computer Engineering Purdue UniversityWest LafayetteINUSA

Recent advances in computer vision and deep learning have shown that the fusion of depth information can significantly enhance the performance of RGB-based damage detection and segmentation ***,alongside the advantages,depth-sensing also presents many practical *** instance,the depth sensors impose an additional payload burden on the robotic inspection platforms limiting the operation time and increasing the inspection ***,some lidar-based depth sensors have poor outdoor performance due to sunlight contamination during the *** this context,this study investigates the feasibility of abolishing depth-sensing at test time without compromising the segmentation *** autonomous damage segmentation framework is developed,based on recent advancements in vision-based multi-modal sensing such as modality hallucination(MH)and monocular depth estimation(MDE),which require depth data only during the model *** the time of deployment,depth data becomes expendable as it can be simulated from the corresponding RGB *** makes it possible to reap the benefits of depth fusion without any depth perception per *** study explored two different depth encoding techniques and three different fusion strategies in addition to a baseline RGB-based *** proposed approach is validated on computer-generated RGB-D data of reinforced concrete buildings subjected to seismic *** was observed that the surrogate techniques can increase the segmentation IoU by up to 20.1%with a negligible increase in the computation ***,this study is believed to make a positive contribution to enhancing the resilience of critical civil infrastructure.

关键词： multimodal data fusion depth sensing vision-based inspection UAV-assisted inspection damage segmentation post-disaster reconnaissance modality hallucination monocular depth estimation

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