35 citations · 94 across the 10 of their papers we have counts for
10 papers
Challenges in Hyperspectral Imaging for Autonomous Driving: The HSI-Drive Case
Koldo Basterretxea, Jon Gutiérrez-Zaballa, Javier Echanobe
The use of hyperspectral imaging (HSI) in autonomous driving (AD), while promising, faces many challenges related to the specifics and requirements of this application domain. On t…
Optimization of DNN-based HSI Segmentation FPGA-based SoC for ADS: A Practical Approach
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe
The use of HSI for autonomous navigation is a promising research field aimed at improving the accuracy and robustness of detection, tracking, and scene understanding systems based…
Balancing Robustness and Efficiency in Embedded DNNs Through Activation Function Selection
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe
Machine learning-based embedded systems for safety-critical applications, such as aerospace and autonomous driving, must be robust to perturbations caused by soft errors. As transi…
Reliable Explainability of Deep Learning Spatial-Spectral Classifiers for Improved Semantic Segmentation in Autonomous Driving
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe
Integrating hyperspectral imagery (HSI) with deep neural networks (DNNs) can strengthen the accuracy of intelligent vision systems by combining spectral and spatial information, wh…
Exploring Fully Convolutional Networks for the Segmentation of Hyperspectral Imaging Applied to Advanced Driver Assistance Systems
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe +2
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 p…
Designing DNNs for a trade-off between robustness and processing performance in embedded devices
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe
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 e…
Evaluating Single Event Upsets in Deep Neural Networks for Semantic Segmentation: an embedded system perspective
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe
As the deployment of artifical intelligence (AI) algorithms at edge devices becomes increasingly prevalent, enhancing the robustness and reliability of autonomous AI-based percepti…
On-chip Hyperspectral Image Segmentation with Fully Convolutional Networks for Scene Understanding in Autonomous Driving
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe +4
Most of current computer vision-based advanced driver assistance systems (ADAS) perform detection and tracking of objects quite successfully under regular conditions. However, unde…
Rapid Deployment of Domain-specific Hyperspectral Image Processors with Application to Autonomous Driving
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe +2
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…
HSI-Drive v2.0: More Data for New Challenges in Scene Understanding for Autonomous Driving
Jon Gutiérrez-Zaballa, Koldo Basterretxea, Javier Echanobe +2
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 annotat…