GMG-LDefmamba-YOLO:一个改进的YOLOv11算法,基于轮状卷积和线性可变形的Mamba模型,用于在无人机图像中检测小物体
Yiming Yang1,2, Lingyu Yan1,2, Jing Wang1,2
1School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
这项研究介绍了GMG-LDefmamba-YOLO,这是一个用于遥感的先进物体检测模型. 它在复杂的环境中提高了小目标检测的准确性和速度,优于现有的方法.
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:
- 遥感和无人机图像中的物体检测至关重要,但受到多尺度,密集的小目标和复杂的环境噪声的挑战.
- 现有的基于Mamba的方法为远程依赖提供了潜力,但需要为遥感应用进行定制.
研究的目的:
- 开发一种高效准确的物体检测模型,适用于遥感和无人机图像分析.
- 解决当前方法在复杂的背景和噪音中检测小,密集的目标方面的局限性.
主要方法:
- 拟议的GMG-LDefmamba-YOLO具有两个核心模块:高斯面具轮卷积,用于增强小目标特征提取和背景噪声抑制.
- 集成了一种线性可变形的Mamba模块,用于适应性目标尺度和空间分布建模,并降低了计算成本.
主要成果:
- 在DOTA-v1.0上获得mAP50分数70.91%,在VEDAI上达到77.94%,在USOD数据集上达到90.28%.
- 与基线和主流物体检测方法相比,表现出卓越的性能.
- 保持轻量级的特性,以实现高效的部署.
结论:
- GMG-LDefmamba-YOLO在遥感和无人机应用中的物体检测方面取得了重大进展.
- 该模型为关键任务提供了高效的技术支持,例如遥感监控和无人机检查.
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