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基于STDC-CT的实时语义细分方法,用于识别无人机紧急着陆区

Bo Jiang¹, Zhonghui Chen¹, Jintao Tan¹

¹College of Air Traffic Management, Civil Aviation Flight University of China, Guanghan 618307, China.

Sensors (Basel, Switzerland)

|July 29, 2023

概括

研究人员开发了一个新的语义细分网络,STDC-CT,用于实时无人机 (UAV) 紧急着陆区识别. 这种方法在复杂的空中成像中提高了小物体的精度,平衡了性能和速度.

科学领域:

计算机视觉计算机视觉
人工智能的人工智能
机器人技术机器人技术机器人技术

背景情况:

无人机飞行安全至关重要,需要紧急降落能力.
安全着陆区的实时识别对于面临GPS丢失或异常的自主无人机至关重要.

研究的目的:

开发一种新的语义细分网络,用于在无人机空中图像中准确有效地识别紧急降落区.
为了应对复杂的背景,多样化的类别和无人机图像中的小目标所带来的挑战.

主要方法:

创建UAV-City数据集,包含12个类别的600张注释空中图像.
关于STDC-CT网络的建议,包括详细指导,小对象关注和多尺度的上下文信息分支.
由小对象的注意力引导的特征分支的融合,以改善细分.

主要成果:

STDC-CT网络在UAV-City,Cityscapes和UAVid数据集上实现了细分精度和推断速度之间的平衡.
在城市景观上以122.6 FPS实现了76.5%的增长率,在UAVid上达到68.4%的增长率,在UAV-City上达到196.8 FPS的67.3%的增长率.
在Jetson TX2.2.上展示了实时性能,平均推断速度为58.32ms/图像.

结论:

STDC-CT网络有效地识别无人机的紧急着陆区域,提高了小物体细分的准确性.
拟议的方法为要求高的无人机应用程序中的实时语义细分提供了强大的解决方案.

关键词:

无人机无人机无人机是什么?深度学习是一种深度学习.这是一个嵌入式设备嵌入式设备.紧急降落区是紧急降落区.实时语义细分实时语义细分

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