Object Recognition in Aerial Images Using Convolutional Neural Networks
Object Recognition in Aerial Images Using Convolutional Neural Networks
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DOI：
10.3390/jimaging3020021
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发表时间：
2017-06-01
影响因子：
3.2
通讯作者：
Wang, Qiaosong
中科院分区：
文献类型：
--
作者：
Radovic, Matija;Adarkwa, Offei;Wang, Qiaosong
在民用基础设施资产的管理中，无人驾驶飞行器（UAV）有许多应用。一些例子包括例行桥梁检查，灾害管理，电力线监测和交通测量。随着无人机应用的广泛，提高自主性和独立决策水平对于提高设备的安全性、效率和准确性是必要的。本文详细介绍了用于在一组航空图像上训练卷积神经网络（CNN）的过程和参数，以实现高效和自动的对象识别。在运输领域的潜在应用领域也被强调。CNN的准确性和可靠性取决于网络的训练和操作参数的选择。本文详细介绍了CNN的训练过程和参数选择。对象识别结果表明，通过选择适当的参数集，CNN可以以高精度（97.5%）和计算效率检测和分类对象。此外，使用在“YOLO”（“You Only Look Once”）平台中实现的卷积神经网络，可以从无人机提供的视频源中实时跟踪、检测（“看到”）和分类（“理解”）对象。
There are numerous applications of unmanned aerial vehicles (UAVs) in the management of civil infrastructure assets. A few examples include routine bridge inspections, disaster management, power line surveillance and traffic surveying. As UAV applications become widespread, increased levels of autonomy and independent decision-making are necessary to improve the safety, efficiency, and accuracy of the devices. This paper details the procedure and parameters used for the training of convolutional neural networks (CNNs) on a set of aerial images for efficient and automated object recognition. Potential application areas in the transportation field are also highlighted. The accuracy and reliability of CNNs depend on the network's training and the selection of operational parameters. This paper details the CNN training procedure and parameter selection. The object recognition results show that by selecting a proper set of parameters, a CNN can detect and classify objects with a high level of accuracy (97.5%) and computational efficiency. Furthermore, using a convolutional neural network implemented in the "YOLO" ("You Only Look Once") platform, objects can be tracked, detected ("seen"), and classified ("comprehended") from video feeds supplied by UAVs in real-time.