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20192022
most citedEnd-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

2 citations · 4 across the 8 of their papers we have counts for

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5 papers · 1 filter

cs.CV2022

Engineering deep learning methods on automatic detection of damage in infrastructure due to extreme events

Yongsheng Bai, Bing Zha, Halil Sezen +1

This paper presents a few comprehensive experimental studies for automated Structural Damage Detection (SDD) in extreme events using deep learning methods for processing 2D images.…

cs.CV2022

Network Comparison Study of Deep Activation Feature Discriminability with Novel Objects

Michael Karnes, Alper Yilmaz

Feature extraction has always been a critical component of the computer vision field. More recently, state-of-the-art computer visions algorithms have incorporated Deep Neural Netw…

cs.CV2021

A volumetric change detection framework using UAV oblique photogrammetry - A case study of ultra-high-resolution monitoring of progressive building collapse

Ningli Xu, Debao Huang, Shuang Song +5

In this paper, we present a case study that performs an unmanned aerial vehicle (UAV) based fine-scale 3D change detection and monitoring of progressive collapse performance of a b…

cs.CV20202 cited

End-to-end Deep Learning Methods for Automated Damage Detection in Extreme Events at Various Scales

Yongsheng Bai, Halil Sezen, Alper Yilmaz

Robust Mask R-CNN (Mask Regional Convolu-tional Neural Network) methods are proposed and tested for automatic detection of cracks on structures or their components that may be dama…

cs.CV2020

Map-Based Temporally Consistent Geolocalization through Learning Motion Trajectories

Bing Zha, Alper Yilmaz

In this paper, we propose a novel trajectory learning method that exploits motion trajectories on topological map using recurrent neural network for temporally consistent geolocali…