无人机在空中输电线路上使用多方向Mamba检测危险
Cheng Xu1, Chunhou Zheng2, Jun Zhang1
1School of Artificial Intelligence, Anhui University, Hefei, 230601, China.
Scientific reports
|October 28, 2025
概括
本研究介绍了UAV-MDMamba用于空中输电线路危险检测,提高准确性和效率. 该模型擅长使用先进的深度学习技术在复杂的背景中识别小规模的危险.
科学领域:
- 电气工程 电气工程
- 计算机视觉 计算机视觉
- 人工智能的人工智能
背景情况:
- 空中输电线路危险检测对于电力系统和社会功能至关重要.
- 无人机和深度学习提供了先进的解决方案,但面临着复杂的背景,小目标和效率等挑战.
- 现有的方法在复杂环境中难以准确有效地检测.
研究的目的:
- 开发一种新的深度学习模型,用于使用无人机增强空中输电线路危险检测.
- 为了解决复杂的背景干扰,小规模检测和效率-性能平衡的局限性.
- 引入基于Mamba的架构,以提高空间建模和检测精度.
主要方法:
- 提出了UAV-MDMamba模型,将Mamba和状态空间模型 (SSM) 纳入线性复杂性.
- 设计了一个多向Mamba (MDMamba) 块,用于更优质的图像空间建模和背景抑制.
- 实施了补丁级推理增强 (PLIE),以提高在推理过程中检测小目标的速度.
主要成果:
- 无人机-MDMamba模型在针对复杂危险场景的新收集和标记数据集上表现出色.
- MDMamba 块有效地捕获了危险区域,特别是在小规模和复杂的背景环境中.
- PLIE显著提高了小型目标的检测精度.
结论:
- 无人机-MDMamba模型在空中输电线路危险检测方面取得了重大进展.
- 这项研究提高了检测输电线路危险的效率和准确性,特别是在具有挑战性的环境中.
- 拟议的方法为使用无人机监控关键基础设施提供了可靠的解决方案.
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