论文标题
使用无人机无人机图像上的深神经网络检测降解的相思树种
Detection of Degraded Acacia tree species using deep neural networks on uav drone imagery
论文作者
论文摘要
基于深度学习的图像分类和对象检测已成功应用于树监视。然而，对树冠和倒下的树木，尤其是在洪水淹没的地区的研究，在很大程度上尚未探索。由于混合的彩色图像背景，检测自然环境（例如水，泥浆和天然植被区域）上降解的树干。在本文中，使用带有嵌入式RGB摄像机的无人驾驶飞机（无人机）或无人机从肯尼亚纳库鲁湖周围的六个指定地捕获了倒下的阿拉相的红藻植物树。由于需要检测湖泊周围倒下的树木的动机，两个建立了良好的深神经网络，即基于区域的卷积神经网络（更快的R-CNN）和视网膜网络被用于倒下的树木检测。本研究使用了256 x 256个图像贴片上三类的总共7,590个注释。实验结果表明，在这种情况下，深度学习的相关性，视网膜网络模型可实现38.9％的精度和57.9％的召回率。
Deep-learning-based image classification and object detection has been applied successfully to tree monitoring. However, studies of tree crowns and fallen trees, especially on flood inundated areas, remain largely unexplored. Detection of degraded tree trunks on natural environments such as water, mudflats, and natural vegetated areas is challenging due to the mixed colour image backgrounds. In this paper, Unmanned Aerial Vehicles (UAVs), or drones, with embedded RGB cameras were used to capture the fallen Acacia Xanthophloea trees from six designated plots around Lake Nakuru, Kenya. Motivated by the need to detect fallen trees around the lake, two well-established deep neural networks, i.e. Faster Region-based Convolution Neural Network (Faster R-CNN) and Retina-Net were used for fallen tree detection. A total of 7,590 annotations of three classes on 256 x 256 image patches were used for this study. Experimental results show the relevance of deep learning in this context, with Retina-Net model achieving 38.9% precision and 57.9% recall.
