Real-time scene understanding for UAV imagery based on deep convolutional neural networks
Real-time scene understanding for UAV imagery based on deep convolutional neural networks
复制标题
基于深度卷积神经网络的无人机图像实时场景理解
DOI：
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
M. Rahnemoonfar
中科院分区：
文献类型：
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作者：
Clay Sheppard;M. Rahnemoonfar
实时场景理解对于无人机 (UAV) 的许多应用（例如侦察、监视、测绘和基础设施检查）非常重要。随着最近计算能力的增长，将深度学习用于实时应用是可行的。深度卷积神经网络 (CNN) 已成为图像内容分类的强大模型，并在计算机视觉社区中被广泛认为是解决大多数问题的事实上的标准方法。当前用于图像分类和目标检测的深度学习方法是在实验室环境下从 1-2 米高度水平拍摄的以人为中心的照片上设计和评估的。无人机高空垂直拍摄图像；因此，感兴趣的物体相对较小，且有利位置倾斜，这对此类图像的检测和分类带来了真正的挑战。在这里，我们提出了一种深度卷积方法，用于对无人机拍摄的航空图像进行分类。我们将我们的网络应用于使用 UAV RS-16 从德克萨斯州曼斯菲尔德港拍摄的光学图像。与地面实况比较的实验结果表明，无人机图像分类的准确率达到 93.6%。
Real-time scene understanding is important for many applications of Unmanned Aerial Vehicles (UAVs) such as reconnaissance, surveillance, mapping, and infrastructure inspection. With the recent growth of computation power, it is feasible to use Deep Learning for real-time applications. Deep Convolutional Neural Networks (CNNs) have emerged as a powerful model for classifying image content, and are widely considered in the computer vision community to be the de facto standard approach for most problems. Current Deep learning approaches for image classification and object detection are designed and evaluated on lab setting human-centric photographs taken horizontally from a height of 1–2 meters. UAV images are taken vertically in high altitude; therefore the objects of interest are relatively small with a skewed vantage point which creates a real challenge in detection and classification of such images. Here we present a deep convolutional approach for classification of Aerial imagery taken by UAV. We applied our network on optical imagery taken with UAV RS-16 from Port Mansfield, TX. Experimental results in comparison with ground-truth show 93.6 % accuracy for UAV image classification.