Categorical-Parallel Adversarial Defense for Perception Models on Single-Board Embedded Unmanned Vehicles

Categorical-Parallel Adversarial Defense for Perception Models on Single-Board Embedded Unmanned Vehicles

Significant advancements in robustness against input perturbations have been realized for deep neural networks (DNNs) through the application of adversarial training techniques. However, implementing these methods for perception tasks in unmanned vehicles, such as object detection and semantic segme...

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Bibliographic Details
Main Authors: Yilan Li, Xing Fan, Shiqi Sun, Yantao Lu, Ning Liu
Format: Article
Language:English
Published: MDPI AG 2024-08-01
Series:Drones
Subjects:
adversarial defense
robustness improvement
single-board computing
unmanned vehicles perception models
Online Access:https://www.mdpi.com/2504-446X/8/9/438

Internet

https://www.mdpi.com/2504-446X/8/9/438

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