ドローン空撮画像からの深層学習によるヤシの木の自動計数と位置推定
Deep-Learning-based Automated Palm Tree Counting and Geolocation in Large Farms from Aerial Geotagged Images
DJIドローンで撮影した空撮画像と約1万件のデータセットを用い、Faster R-CNNでヤシの木を検出し、写真測量と距離補正により平均2.8mの精度でGPS位置を自動推定する手法を開発した。
著者: Adel Ammar, Anis Koubaa
分類: cs.CV, cs.LG, cs.RO, eess.IV
原文アブストラクト
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.
