基于TGE-YOLO的台风本地化检测算法
Lan He1, Ling Xiao1, Peihao Yang2
1College of Mathematics and Computer Science, Guangdong Ocean University, Zhanjiang, 524088, China.
Scientific reports
|January 27, 2025
概括
这项研究介绍了TGE-YOLO,这是一种新的台风定位算法,可以提高卫星云图的准确性和效率. 新型号改进了台风识别和中心定位,有助于实时监测和警告.
科学领域:
- 气象学和大气科学 气象学和大气科学
- 计算机视觉和机器学习
- 遥感技术 遥感技术 遥感技术
背景情况:
- 卫星云地图在准确识别复杂云特征方面存在挑战,导致台风定位和检测不准确.
- 现有的算法难以平衡特征融合,计算效率和精确的局部化来进行台风分析.
研究的目的:
- 开发一个先进的台风定位算法,TGE-YOLO,克服当前基于卫星的台风识别方法的局限性.
- 通过使用深度学习,提高台风中心定位的准确性和提高整体目标检测性能.
主要方法:
- TGE-YOLO算法是基于YOLOv8n模型构建的,在子网络中结合了一种新的TFAM_Concat模块,以实现卓越的特征融合.
- 它使用GSConv卷积来降低计算负载并改善全球信息聚合,以及改进的高效交叉对联 (EIoU) 损失函数,以提高位置精度.
主要成果:
- TGE-YOLO实现了87.8%的台风识别平均精度 (mAP),超过了Faster R-CNN,YOLOv5s,YOLOv9s和YOLOv11n.
- 该模型展示了台风中心定位的0.115的平均平方误差 (MSE) 和每秒416.7 (FPS) 的检测速度.
结论:
- TGE-YOLO模型在台风定位和检测准确性和速度方面取得了重大进展.
- 它的高效性能为实时台风监测提供了关键的技术支持,承诺更及时,更准确的台风警告.
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