Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles

Leveraging Monte Carlo Dropout for Uncertainty Quantification in Real-Time Object Detection of Autonomous Vehicles

With the recent advancements in machine learning technology, the accuracy of autonomous driving object detection models has significantly improved. However, due to the complexity and variability of real-world traffic scenarios, such as extreme weather conditions, unconventional lighting, and unknown...

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Bibliographic Details
Main Authors: Rui Zhao, Kui Wang, Yang Xiao, Fei Gao, Zhenhai Gao
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Uncertainty quantification
object detection
autonomous vehicles
YOLOv5
Monte Carlo dropout
Online Access:https://ieeexplore.ieee.org/document/10401930/

Internet

https://ieeexplore.ieee.org/document/10401930/

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