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一个超轻量级的探测器,具有高准确性和飞行图像的速度

Lei Yang¹, Guowu Yuan^1,2, Hao Wu^1,2

¹School of Information Science and Engineering, Yunnan University, Kunming 650504, Yunnan, China.

Mathematical biosciences and engineering : MBE

|September 7, 2023

概括

这项研究引入了一种新的YOLO模型用于空中图像目标检测,平衡高精度和速度. 这种轻量级模型可以提高资源勘探和城市规划等应用的检测性能.

科学领域:

计算机视觉计算机视觉
遥感技术遥感技术遥感技术
人工智能的人工智能

背景情况:

由于复杂的背景和小目标,空中遥感图像存在挑战,阻碍了准确和快速的检测.
现有的检测模型经常面临高精度 (复杂,计算密集) 和速度 (轻量级,不那么准确) 之间的权衡.

研究的目的:

为空中成像开发一种轻量级的物体检测模型,在精度和检测速度之间实现卓越的平衡.
提高远程传感应用中目标检测的效率和有效性.

主要方法:

提出了一个新的YOLO模型,集成YOLOX-Nano和细结构.
包含了SPPF模块和SIoU损失函数.
引入了一种结合线性互插和注意力机制以提高准确度的新型上样方法.

主要成果:

与原来的YOLOX-Nano相比,拟议的模型显示了准确性和速度之间的更好的平衡.
在包括NWPU VHR-10,RSOD,TGRS-HRRSD和DOTA在内的基准数据集上实现了高精度和速度.
保持了适合高效部署的轻量级架构.

结论:

开发的YOLO模型有效地解决了空中图像目标检测中的准确性-速度权衡问题.
该模型显示了在资源勘探和城市规划中实际应用的巨大潜力,需要快速准确的空中图像分析.

关键词:

这是一个YOLOX.航空图像航空图像深度学习是一种深度学习.轻量级的模型轻量级的模型.对象检测检测对象检测对象检测

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