论文标题
大型农场的深度学习自动化棕榈树计数和地理位置
Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images
论文作者
论文摘要
在本文中，我们为使用卷积神经网络从空中图像中对棕榈树进行自动计数和地理位置的深度学习框架。为此，我们使用DJI无人机在卡尔吉地区的一个棕榈树农场收集了空中图像，我们建造了一个大约10,000个棕榈树实例的数据集。然后，我们使用最先进的R-CNN算法开发了卷积神经网络模型。此外，使用空中图像的地理标记元数据，我们使用摄影测量概念和距离校正来自动检测检测到的棕榈树的地理位置。该地理位置技术已在两种不同类型的无人机（DJI Mavic Pro和Phantom 4 Pro）上进行了测试，并经过评估以提供平均地理位置精度为280万。这种GPS标记使我们能够唯一地识别棕榈树并从一系列无人机图像中计算它们的数字，同时正确处理了图像重叠问题。此外，它可以推广到无人机图像中任何其他对象的地理位置。
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.
