Image segmentation for automated taxiing of Unmanned Aircraft
Image segmentation for automated taxiing of Unmanned Aircraft
复制标题
无人机自动滑行的图像分割
DOI：
10.1109/icuas.2015.7152268
复制
发表时间：
2015
期刊：
影响因子：
--
通讯作者：
Wen‐Hua Chen
中科院分区：
文献类型：
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作者：
William Eaton;Wen‐Hua Chen
本文详细介绍了一种无人机滑行过程中碰撞风险的检测方法。使用从车载摄像头捕获的图像，语义分割可用于识别表面类型并检测潜在的碰撞。分类器引线分割的审查得出结论，纹理特征描述符缺乏避免冲突所需的像素级精度。相反，分割前分类建议作为一个更好的方法，准确的区域边界提取。这是通过使用已建立的SLIC超像素技术的初始过分割以及使用DBSCAN算法的进一步未经训练的聚类来实现的。已知的类用于通过构造每个类典型的文本基元字典和文本基元内容的模型来训练分类器。本文演示了该系统的应用，真实的世界的图像，并显示了良好的自动分割识别。确定了剩余的问题，并建议将背景信息作为今后解决这些问题的方法。
This paper details a method of detecting collision risks for Unmanned Aircraft during taxiing. Using images captured from an on-board camera, semantic segmentation can be used to identify surface types and detect potential collisions. A review of classifier lead segmentation concludes that texture feature descriptors lack the pixel level accuracy required for collision avoidance. Instead, segmentation prior to classification is suggested as a better method for accurate region border extraction. This is achieved through an initial over-segmentation using the established SLIC superpixel technique with further untrained clustering using DBSCAN algorithm. Known classes are used to train a classifier through construction of a texton dictionary and models of texton content typical to each class. The paper demonstrates the application of said system to real world images, and shows good automated segment identification. Remaining issues are identified and contextual information is suggested as a method of resolving them going forward.