无人机
计算机科学
人工智能
卷积神经网络
计算机视觉
航空影像
代表(政治)
特征(语言学)
图像(数学)
实时计算
遗传学
生物
语言学
哲学
政治
政治学
法学
作者
Oğuz Bektaş,Jacob Juul Naundrup,Anders la Cour-Harbo
标识
DOI:10.1177/17568293221106492
摘要
Autonomous landing is a fundamental aspect of drone operations which is being focused upon by the industry, with ever-increasing demands on safety. As the drones are likely to become indispensable vehicles in near future, they are expected to succeed in automatically recognizing a landing spot from the nearby points, maneuvering toward it, and ultimately, performing a safe landing. Accordingly, this paper investigates the idea of vision-based location detection on the ground for an automated emergency response system which can continuously monitor the environment and spot safe places when needed. A convolutional neural network which learns from image-based feature representation at multiple scales is introduced. The model takes the ground images, assign significance to various aspects in them and recognize the landing spots. The results provided support for the model, with accurate classification of ground image according to their visual content. They also demonstrate the feasibility of computationally inexpensive implementation of the model on a small computer that can be easily embedded on a drone.
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