Extensive experimental results of our R-YOLOv3, R-YOLOv5, and R-YOLOX on both hazy and rainy datasets show that our method outperforms other detectors with dehaze/derain as the pre-processing step and other unsupervised domain adaptation-based detectors, which confirms the effectiveness of our method on improving the ...
Dec 15, 2022 · In this article, we propose a framework to improve the YOLO to a robust detector, denoted as R(obust)-YOLO, without the need for annotations in ...
A robust object detector in adverse weather. Here, we provide detailed instructions to help replay our experiments. To make it easy to reproduce our research ...
This paper proposes a novel framework, R-YOLO (Robust-. YOLO), which addresses the limitations of existing approaches by employing unsupervised domain.
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Mar 14, 2024 · It is a robust detection framework for bridging the gap between low-level dehazing and high-level detection.
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This article proposes a framework to improve the YOLO to a robust detector, denoted as R(obust)-YOLO, without the need for annotations in adverse weather, ...
R-. YOLO(Robust-YOLO) utilizes solo space variation to connect the area hole among ordinary and extreme climate conditions, tending to the impediments of.
May 13, 2024 · R-YOLO: A Robust Object Detector in Adverse Weather. Authors. RAMESH BABU K. Author; VENKATESH PASUPULETI Author. Keywords: Adversarial learning ...
We proposes a framework called R-YOLO for improving the robustness of the YOLO object detector in adverse weather conditions without the need for annotations.