2022
DOI: 10.1109/tim.2022.3229717
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R-YOLO: A Robust Object Detector in Adverse Weather

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Cited by 17 publications
(10 citation statements)
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“…Although YOLO decreased slightly by 0.2% in the variable 𝐴𝑃𝑀 21 compared to the Libra R-CNN model, the network structure was improved with the gradual introduction of YOLO9000 22 and YOLOv5 in the YOLO series. Compared with the traditional depth model, the evaluation indexes of YOLOv5 model are improved, 16,23 AP50 is increased by 18.1%, 24 the experimental results show that the dense target identification is more accurate. The recognition accuracy was 97.2%, 96.2%, and 97.9%, respectively.…”
Section: Deep Learning Modelsmentioning
confidence: 96%
“…Although YOLO decreased slightly by 0.2% in the variable 𝐴𝑃𝑀 21 compared to the Libra R-CNN model, the network structure was improved with the gradual introduction of YOLO9000 22 and YOLOv5 in the YOLO series. Compared with the traditional depth model, the evaluation indexes of YOLOv5 model are improved, 16,23 AP50 is increased by 18.1%, 24 the experimental results show that the dense target identification is more accurate. The recognition accuracy was 97.2%, 96.2%, and 97.9%, respectively.…”
Section: Deep Learning Modelsmentioning
confidence: 96%
“…Additionally, our feasibility experiments were conducted in various environments, but the model's performance was still influenced by environmental factors. For instance, in overcast nighttime conditions, the recognition accuracy of the YOLOv5s-KCV model dropped to its lowest [38].…”
Section: Advantages and Limitationsmentioning
confidence: 99%
“…Object detection in adverse weather conditions include fog, nighttime, rain and snow remains challenging due to the obscurity or absence of salient object features, noise from weather patterns, and weak ambient illumination. Wang et al introduced R-YOLO [15], an object detection system that is resilient to challenging weather conditions. This architecture comprises a quasitranslation network (QTNet) for images and a feature calibration network (FCNet) that gradually adapts the normal weather domain to the adverse weather domain.…”
Section: Related Workmentioning
confidence: 99%