基于深度学习的大型农场棕榈树自动计数和地理定位
Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images
摘要：
在本文中，我们提出了一个深度学习框架，用于使用卷积神经网络从航空图像中自动计数和地理定位棕榈树。为此，我们使用DJI无人机在沙特阿拉伯利雅得的Kharj地区的棕榈树农场收集了航空图像，并建立了一个包含约10，000棵棕榈树的数据集。然后，我们使用最先进的Faster R-CNN算法开发了一个卷积神经网络模型。此外，使用航空影像的地理标记的元数据，我们使用摄影测量的概念和距离校正来自动检测检测到的棕榈树的地理位置。这种地理定位技术在两种不同类型的无人机（DJI Mavic Pro和Phantom 4 Pro）上进行了测试，并被评估为提供平均2.8米的地理定位精度。这种GPS标记使我们能够从一系列无人机图像中唯一识别棕榈树并计算它们的数量，同时正确处理图像重叠的问题。此外，它可以推广到无人机图像中的任何其他对象的地理定位。
英文摘要：
In this paper, we propose a deep learning framework for the automated counting and geolocation of palm trees from aerial images using convolutional neural networks. For this purpose, we collected aerial images in a palm tree Farm in the Kharj region, in Riyadh Saudi Arabia, using DJI drones, and we built a dataset of around 10,000 instances of palms trees. Then, we developed a convolutional neural network model using the state-of-the-art, Faster R-CNN algorithm. Furthermore, using the geotagged metadata of aerial images, we used photogrammetry concepts and distance corrections to detect the geographical location of detected palms trees automatically. This geolocation technique was tested on two different types of drones (DJI Mavic Pro, and Phantom 4 Pro), and was assessed to provide an average geolocation accuracy of 2.8m. This GPS tagging allows us to uniquely identify palm trees and count their number from a series of drone images, while correctly dealing with the issue of image overlapping. Moreover, it can be generalized to the geolocation of any other objects in UAV images.
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