
Multi-label deep learning models for continuous monitoring of road infrastructures | Proceedings of the 13th ACM International Conference on PErvasive Technologies Related to Assistive Environments
research-article Multi-label deep learning models for continuous monitoring of road infrastructures Share on Authors: Eftychios Protopapadakis University of Athens, Greece University of Athens, GreeceView Profile , Iason Katsamenis University of Athens, Greece University of Athens, GreeceView Profil...
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Deep learning models for continuous monitoring of road infrastructures
A multi-class, multi-label deep learning model for the monitoring of road infrastructures is presented in this paper. The employed detection methodology can ...
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A Seismic Design Procedure for Different Performance Objectives for Post-Tensioned Walls
(2019). A Seismic Design Procedure for Different Performance Objectives for Post-Tensioned Walls. Journal of Earthquake Engineering. Ahead of Print.
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DETECTING POTHOLES using Computer Vision and Machine Learning
Potholes are one of the largest problems every city faces around the world. And it is getting worse as citizens are filing complaints to claim damages to the...
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Drone Wind Turbine and Blade Inspection, Maintenance, Survey - ABJ Drones
Drone Wind Farm, Turbine and Blade Inspection, Maintenance, and Survey: https://abjdrones.com/drone-wind-turbine-inspection/Effective way to inspect and mana...
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Prediction of the agronomy through Artificial Intelligence.
Latest Technology,science,AR,VR,Artificial Intelligence(AI),Machine learning(ML) Robotics,Internet of Things(IOT),Medical Technology,Block Chain...
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This project has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No  769129