极地研究2025，Vol.37Issue(1)：1-10,10.DOI:10.13679/j.jdyj.20230017
基于YOLOv5s模型的北极气旋目标检测方法
Arctic cyclone detection method based on the YOLOv5s model
摘要
Abstract
Arctic cyclones are one of the main weather systems to affect the Arctic environment.Accurate identi-fication of Arctic cyclones is of crucial importance to Arctic navigational support.Based on mean sea level pressure data from the new generation European Center for Medium-range Weather Forecasts ERA5 re-analysis product,this study constructed a dataset for Arctic cyclone target detection and trained the deep learning target detection You Only Look Once version 5(YOLOv5s)model for Arctic cyclone recognition,the performance of which was verified through comparison with other deep learning object detection models(i.e.,the Single Shot Multibox Detector,Faster-RCNN,and You Only Look Once version 4 models).Ex-perimental results showed that compared with other deep learning models,the detection accuracy,average accuracy,and average detection speed of YOLOv5s were 95.26%,98.09%,and 64.85 s-1,respectively,and that detection speed increased by 15.65 s-1.This improved performance means that YOLOv5s can effec-tively identify Arctic cyclone targets.Based on the detection and recognition results of the YOLOv5s model,Arctic cyclones that occurred in winter and summer 2021 were selected for case analysis.The life cycle and average intensity of an Arctic cyclone generated at 12:00 on 8 March(winter)were 48 hours and 54.62,respectively,and those of an Arctic cyclone generated at 06:00 on 21 September(summer)were 84 h and 42.82,respectively.These findings are consistent with observed Arctic cyclones,i.e.,long and weak in summer,short and strong in winter.The YOLOv5s model provides new approaches and ideas for detection and identification of Arctic cyclones.关键词
北极/北极气旋识别/ERA5/深度学习/YOLOv5s模型Key words
Arctic/Arctic cyclone identification/ERA5/deep learning/YOLOv5s model引用本文复制引用
王钰坤,谢涛,向儒萱毅,张雪红,白淑英..基于YOLOv5s模型的北极气旋目标检测方法[J].极地研究,2025,37(1):1-10,10.基金项目
国家重点研发计划(2021YFC2803302、2022YFC3004200/2022YFC3004202、2022YFC3104900/2022YFC3104905)、国家自然科学基金(42176180)、江苏省自然资源发展专项资金(海洋科技创新)项目(JSZRHYKJ202114)和江苏省研究生科研创新计划(KYCX23_1345)资助 （2021YFC2803302、2022YFC3004200/2022YFC3004202、2022YFC3104900/2022YFC3104905）
