Unmanned ground operations using semantic image segmentation through a Bayesian network
Unmanned ground operations using semantic image segmentation through a Bayesian network
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DOI：
10.1109/icuas.2016.7502572
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发表时间：
2016-07
期刊：
影响因子：
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通讯作者：
M. Coombes;William Eaton;Wen‐Hua Chen
中科院分区：
文献类型：
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作者：
M. Coombes;William Eaton;Wen‐Hua Chen
本文讨论了一个系统的机器视觉元素，设计允许自动滑行的无人机系统（UAS）周围的民用航空器。计算机视觉系统的目的是提供可用于验证车辆位置的直接传感器数据，以及检测潜在的碰撞风险。这是通过使用单一的单目传感器来实现的。在每个聚类的描述符（主要是颜色和纹理）用于估计类别之前，使用未经训练的聚类来分割视觉馈送。由于每个单独估计的能力可以基于多个因素（像素数量、照明条件甚至表面类型）而变化，因此使用贝叶斯网络来执行概率数据融合，以改善分类结果。这一结果表明，在现实世界的条件下进行准确的图像分割，提供信息可行的地图匹配。
This paper discusses the machine vision element of a system designed to allow automated taxiing for Unmanned Aerial System (UAS) around civil aerodromes. The purpose of the computer vision system is to provide direct sensor data which can be used to validate vehicle position, in addition to detect potential collision risks. This is achieved through the use of a singular monocular sensor. Untrained clustering is used to segment the visual feed before descriptors of each cluster (primarily colour and texture) are then used to estimate the class. As the competency of each individual estimate can vary based on multiple factors (number of pixels, lighting conditions and even surface type) a Bayesian network is used to perform probabilistic data fusion, in order to improve the classification results. This result is shown to perform accurate image segmentation in real-world conditions, providing information viable for map matching.