V
Vedhus Hoskere
Researcher at University of Illinois at Urbana–Champaign
Publications - 29
Citations - 1104
Vedhus Hoskere is an academic researcher from University of Illinois at Urbana–Champaign. The author has contributed to research in topics: Computer science & Deep learning. The author has an hindex of 10, co-authored 20 publications receiving 453 citations. Previous affiliations of Vedhus Hoskere include University of Houston.
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Journal ArticleDOI
Advances in Computer Vision-Based Civil Infrastructure Inspection and Monitoring
TL;DR: An overview of recent advances in computer vision techniques as they apply to the problem of civil infrastructure condition assessment and some of the key challenges that persist toward the goal of automated vision-based civil infrastructure and monitoring are presented.
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Vision-Based Modal Survey of Civil Infrastructure Using Unmanned Aerial Vehicles
TL;DR: This data indicates that dynamic structural displacements from videos are being extracted from systems for the purposes of system identification and structural health monitoring in an efficient and scalable manner.
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Vision-based automated bridge component recognition with high-level scene consistency
TL;DR: This research investigates vision‐based automated bridge component recognition, which is critical for automating visual inspection of bridges during initial response after earthquakes, and combines 10‐class scene classification and 5‐class bridge component classification.
Journal ArticleDOI
Cross-Correlation-Based Structural System Identification Using Unmanned Aerial Vehicles.
TL;DR: A new method for structural system identification using the UAV video directly, which addresses the issue of the camera itself moving and several challenges are addressed, including: estimation of an appropriate scale factor; and compensation for the rolling shutter effect.
Posted Content
Vision-based Structural Inspection using Multiscale Deep Convolutional Neural Networks.
TL;DR: A novel damage localization and classification technique based on a state of the art computer vision algorithm is presented to address several key limitations of current computer vision techniques.