52 citations · 131 across the 13 of their papers we have counts for
13 papers
Towards Real-Time PixOOD: Efficient Anomaly Segmentation for Autonomous Vehicles
Luca de Martino, Federico Aromolo, Federico Nesti +1
Real-time anomaly segmentation is essential for the safety of autonomous systems. Although recent approaches offer high accuracy, their computational cost limits their deployment o…
Integrating Object Detection, LiDAR-Enhanced Depth Estimation, and Segmentation Models for Railway Environments
Enrico Francesco Giannico, Federico Nesti, Gianluca D'Amico +5
Obstacle detection in railway environments is crucial for ensuring safety. However, very few studies address the problem using a complete, modular, and flexible system that can bot…
OSDaR-AR: Enhancing Railway Perception Datasets via Multi-modal Augmented Reality
Federico Nesti, Gianluca D'Amico, Mauro Marinoni +1
Although deep learning has significantly advanced the perception capabilities of intelligent transportation systems, railway applications continue to suffer from a scarcity of high…
The Use of the Simplex Architecture to Enhance Safety in Deep-Learning-Powered Autonomous Systems
Federico Nesti, Niko Salamini, Mauro Marinoni +4
Recently, the outstanding performance reached by neural networks in many tasks has led to their deployment in autonomous systems, such as robots and vehicles. However, neural netwo…
Towards Railway Domain Adaptation for LiDAR-based 3D Detection: Road-to-Rail and Sim-to-Real via SynDRA-BBox
Xavier Diaz, Gianluca D'Amico, Raul Dominguez-Sanchez +3
In recent years, interest in automatic train operations has significantly increased. To enable advanced functionalities, robust vision-based algorithms are essential for perceiving…
SimPRIVE: a Simulation framework for Physical Robot Interaction with Virtual Environments
Federico Nesti, Gianluca D'Amico, Mauro Marinoni +1
The use of machine learning in cyber-physical systems has attracted the interest of both industry and academia. However, no general solution has yet been found against the unpredic…
Feelbert: A Feedback Linearization-based Embedded Real-Time Quadrupedal Locomotion Framework
Aristide Emanuele Casucci, Federico Nesti, Mauro Marinoni +1
Quadruped robots have become quite popular for their ability to adapt their locomotion to generic uneven terrains. For this reason, over time, several frameworks for quadrupedal lo…
TrainSim: A Railway Simulation Framework for LiDAR and Camera Dataset Generation
Gianluca D'Amico, Mauro Marinoni, Federico Nesti +4
The railway industry is searching for new ways to automate a number of complex train functions, such as object detection, track discrimination, and accurate train positioning, whic…
CARLA-GeAR: a Dataset Generator for a Systematic Evaluation of Adversarial Robustness of Vision Models
Federico Nesti, Giulio Rossolini, Gianluca D'Amico +2
Adversarial examples represent a serious threat for deep neural networks in several application domains and a huge amount of work has been produced to investigate them and mitigate…
Defending From Physically-Realizable Adversarial Attacks Through Internal Over-Activation Analysis
Giulio Rossolini, Federico Nesti, Fabio Brau +2
This work presents Z-Mask, a robust and effective strategy to improve the adversarial robustness of convolutional networks against physically-realizable adversarial attacks. The pr…
On the Real-World Adversarial Robustness of Real-Time Semantic Segmentation Models for Autonomous Driving
Giulio Rossolini, Federico Nesti, Gianluca D'Amico +3
The existence of real-world adversarial examples (commonly in the form of patches) poses a serious threat for the use of deep learning models in safety-critical computer vision tas…
Evaluating the Robustness of Semantic Segmentation for Autonomous Driving against Real-World Adversarial Patch Attacks
Federico Nesti, Giulio Rossolini, Saasha Nair +2
Deep learning and convolutional neural networks allow achieving impressive performance in computer vision tasks, such as object detection and semantic segmentation (SS). However, r…
Detecting Adversarial Examples by Input Transformations, Defense Perturbations, and Voting
Federico Nesti, Alessandro Biondi, Giorgio Buttazzo
Over the last few years, convolutional neural networks (CNNs) have proved to reach super-human performance in visual recognition tasks. However, CNNs can easily be fooled by advers…