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霍夫变换和Canny边缘检测方法在积状云视觉检测中的应用

The Application of Hough Transform and Canny Edge Detector Methods for the Visual Detection of Cumuliform Clouds.

作者信息

Lapušinskij Aleksandr, Suzdalev Ivan, Goranin Nikolaj, Janulevičius Justinas, Ramanauskaitė Simona, Stankūnavičius Gintautas

机构信息

Antanas Gustaitis Aviation Institute, Vilnius Gediminas Technical University, LT-08217 Vilnius, Lithuania.

Faculty of Fundamental Sciences, Vilnius Gediminas Technical University, LT-10223 Vilnius, Lithuania.

出版信息

Sensors (Basel). 2021 Aug 29;21(17):5821. doi: 10.3390/s21175821.

Abstract

The increase in flying time of unmanned aerial vehicles (UAV) is a relevant and difficult task for UAV designers. It is especially important in such tasks as monitoring, mapping, or signal retranslation. While the majority of research is concentrated on increasing the battery capacity, it is also important to utilize natural renewable energy sources, such as solar energy, thermals, etc. This article proposed a method for the automatic recognition of cumuliform clouds. Practical application of this method allows diverting of an unmanned aerial vehicle towards the identified cumuliform cloud and improving its probability of flying into a thermal flow, thus increasing the flight time of the UAV, as is performed by glider and paraglider pilots. The proposed method is based on the application of Hough transform and Canny edge detector methods, which have not been used for such a task before. For testing the proposed method a dataset of different clouds was generated and marked by experts. The achieved average accuracy of 87% on the unbalanced dataset demonstrates the practical applicability of the proposed method for detecting thermals related to cumuliform clouds. The article also provides the concept of VilniusTech developed UAV, implementing the proposed method.

摘要

无人机飞行时间的增加对无人机设计师来说是一项相关且困难的任务。在监测、测绘或信号中继等任务中尤为重要。虽然大多数研究都集中在增加电池容量上,但利用太阳能、热气流等自然可再生能源也很重要。本文提出了一种自动识别积状云的方法。该方法的实际应用可使无人机转向已识别的积状云,并提高其飞入热气流的概率,从而增加无人机的飞行时间,就像滑翔机和悬挂式滑翔机飞行员所做的那样。所提出的方法基于霍夫变换和Canny边缘检测方法的应用,此前这些方法尚未用于此类任务。为了测试所提出的方法,生成了一个由专家标记的不同云层数据集。在不平衡数据集上达到的87%的平均准确率证明了所提出的方法在检测与积状云相关的热气流方面的实际适用性。本文还介绍了维尔纽斯科技大学开发的实现该方法的无人机概念。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5abd/8434448/034a10d922cd/sensors-21-05821-g001.jpg

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