论文标题
自动无人机的重量轻巧和形状识别
Light Weight Character and Shape Recognition for Autonomous Drones
论文作者
论文摘要
在搜救任务中，广泛使用无人驾驶汽车来分发急救工具包和食品包。重要的是，这些无人机能够识别和区分标记以进行有效分布。标记位置的常见方法之一是使用在各种颜色的形状上叠加的字符，这些字符基于不同形状，角色及其各自颜色的组合而产生各种标记。 在本文中，我们提出了一个对象检测和分类管道，该管道可防止误报，并最大程度地减少对空中图像中字母数字字符和形状的错误分类。我们的方法利用传统的计算机视觉技术和无监督的机器学习方法来识别区域建议，分割图像目标并删除误报。我们利用计算轻型模型进行分类，使其易于部署在任何航空车上。
There has been an extensive use of Unmanned Aerial Vehicles in search and rescue missions to distribute first aid kits and food packets. It is important that these UAVs are able to identify and distinguish the markers from one another for effective distribution. One of the common ways to mark the locations is via the use of characters superimposed on shapes of various colors which gives rise to wide variety of markers based on combination of different shapes, characters, and their respective colors. In this paper, we propose an object detection and classification pipeline which prevents false positives and minimizes misclassification of alphanumeric characters and shapes in aerial images. Our method makes use of traditional computer vision techniques and unsupervised machine learning methods for identifying region proposals, segmenting the image targets and removing false positives. We make use of a computationally light model for classification, making it easy to be deployed on any aerial vehicle.
