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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Hlavní autoři:	Rui Zhao, Kui Wang, Yang Xiao, Fei Gao, Zhenhai Gao
Médium:	Artigo
Jazyk:	Inglês
Vydáno:	IEEE 2024-01-01
Edice:	IEEE Access
Témata:	Uncertainty quantification object detection autonomous vehicles YOLOv5 Monte Carlo dropout
On-line přístup:	https://ieeexplore.ieee.org/document/10401930/
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