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首页> 外文期刊>Journal of Aerospace Computing, Information, and Communication >Machine Vision Identification of Airport Runways with Visible and Infrared Videos
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Machine Vision Identification of Airport Runways with Visible and Infrared Videos

机译：具有可见和红外视频的机场跑道机器视觉识别

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摘要

A widely used machine vision pipeline based on the Speeded-Up Robust Features feature detector was applied to the problem of identifying a runway from a universe of known runways, which was constructed using video records of 19 straight-in glidepath approaches to nine runways. The recordings studied included visible, short-wave infrared, and long-wave infrared videos in clear conditions, rain, and fog. Both daytime and nighttime runway approaches were used. High detection specificity (identification of the runway approached and rejection of the other runways in the universe) was observed in all conditions (greater than 90% Bayesian posterior probability). In the visible band, repeatability (identification of a given runway across multiple videos of it) was observed only if illumination (day versus night) was the same and approach visibility was good. Some repeatability was found across visible and shortwave sensor bands. Camera-based geolocation during aircraft landing was compared to the standard Charted Visual Approach Procedure.
机译：基于加速健壮特征检测器的广泛使用的机器视觉管道已应用于从已知跑道的范围中识别跑道的问题，该跑道是使用19条直滑滑道进近9条跑道的视频记录构建的。研究的记录包括在清晰的条件下，下雨和下雾的可见，短波红外和长波红外视频。白天和晚上都使用跑道进场。在所有条件下（大于90％的贝叶斯后验概率）都观察到了很高的检测特异性（确定了正在接近的跑道并拒绝了宇宙中其他跑道）。在可见波段中，只有在照度（白天与黑夜）相同且进场可见度良好的情况下，才能观察到可重复性（在跑道的多个视频中对给定跑道的识别）。在可见光和短波传感器频段上发现了一些可重复性。将飞机降落过程中基于摄像头的地理位置与标准的“视觉进近程序”进行了比较。

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