BackBrowsing by Author "Echanove Arias, Francisco Javier"
Now showing items 1-9 of 9
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Deep Extreme Learning Machines with Auto Encoder for Speed Limit Signs Recognition
Mata Carballeira, Oscar
; Del Campo Hagelstrom, Inés Juliana ; ; Echanove Arias, Francisco Javier (IEEE, 2018-12-09)
This work presents a Deep Extreme Learning Machine with Auto Encoder scheme for Speed Limit Signs Recognition in the field of Advanced Driving Assistance Systems, where traffic sign recognition from video imaging plays an ... -
Designing DNNs for a trade-off between robustness and processing performance in embedded devices
; ; Echanove Arias, Francisco Javier (IEEE, 2024-12-03)
Machine learning-based embedded systems employed in safety-critical applications such as aerospace and autonomous driving need to be robust against perturbations produced by soft errors. Soft errors are an increasing concern ... -
Driving Style Recognition based on Ride Comfort Using a Hybrid Machine Learning Algorithm
Del Campo Hagelstrom, Inés Juliana; Asua Uriarte, Estibaliz ; ; Mata Carballeira, Oscar ; Echanove Arias, Francisco Javier (IEEE, 2018-12-09)
Driving style (DS) classification and identification plays an increasingly important role in the development of advanced driver assistance systems and automated vehicles. Both the enhancement of driving safety and the ... -
Evaluating single event upsets in deep neural networks for semantic segmentation: An embedded system perspective
; ; Echanove Arias, Francisco Javier (Elsevier, 2024-09)
As the deployment of artificial intelligence (AI) algorithms at edge devices becomes increasingly prevalent, enhancing the robustness and reliability of autonomous AI-based perception and decision systems is becoming as ... -
Exploring fully convolutional networks for the segmentation of hyperspectral imaging applied to advanced driver assistance systems
; ; Echanove Arias, Francisco Javier; ; Del Campo Hagelstrom, Inés Juliana (Springer, 2022-07-30)
Advanced Driver Assistance Systems (ADAS) are designed with the main purpose of increasing the safety and comfort of vehicle occupants. Most of current computer vision-based ADAS perform detection and tracking tasks quite ... -
HSI-Drive v2. 0: More Data for New Challenges in Scene Understanding for Autonomous Driving
; ; Echanove Arias, Francisco Javier; ; Martínez Corral, Unai (IEEE, 2024-01-01)
We present the updated version of the HSI-Drive dataset aimed at developing automated driving systems (ADS) using hyperspectral imaging (HSI). The v2.0 version includes new annotated images from videos recorded during ... -
On-chip hyperspectral image segmentation with fully convolutional networks for scene understanding in autonomous driving
; ; Echanove Arias, Francisco Javier; ; Martínez Corral, Unai ; Mata Carballeira, Oscar ; Del Campo Hagelstrom, Inés Juliana (Elsevier, 2023-06)
Most of current computer vision-based advanced driver assistance systems (ADAS) perform detection and tracking of objects quite successfully under regular conditions. However, under adverse weather and changing lighting ... -
Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving
; ; Echanove Arias, Francisco Javier; Mata Carballeira, Oscar ; (IEEE, 2024-01-10)
The article discusses the use of low cost System-On-Module (SOM) platforms for the implementation of efficient hyperspectral imaging (HSI) processors for application in autonomous driving. The work addresses the challenges ... -
Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving
; ; Echanove Arias, Francisco Javier (IEEE, 2025-02-19)
Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, which is useful for tasks like semantic ...