Robust Object Detection Against Multi-Type Corruption Without Catastrophic Forgetting During Adversarial Training Under Harsh Autonomous-Driving Environments

It is important to build robust object detector (ROD) in real-world applications because snow, rain, fog, motion blur, and various kinds of corruption can occur in autonomous-driving environments. Adversarial training (AT) is one of the best solutions to build a robust deep neural network. However,...

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Bibliographic Details

Published in:	IEEE Access
Main Authors:	Youngjun Kim, Jitae Shin
Format:	Article
Language:	English
Published:	IEEE 2023-01-01
Subjects:	Autonomous-driving robust object detection adversarial training corruption catastrophic forgetting feature-level denoising
Online Access:	https://ieeexplore.ieee.org/document/10075553/