利用更快的基于区域的卷积神经网络实现航拍图像中的实时房屋检测

IF 0.4 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Khandaker Mamun Ahmed, Farid Ghareh Mohammadi, M. Matus, Farzan Shenavarmasouleh, Luiz Manella Pereira, Zisis Ioannis, M. Amini
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引用次数: 0

摘要

近年来,航拍图像中的建筑物自动检测已成为计算机视觉中的一个新兴领域。检测特定类型的房屋将为城市化、变化检测和城市监测提供信息,在现代城市规划和自然灾害防范中发挥越来越重要的作用。在本文中,我们证明了使用Faster- rcnn快速区域卷积神经网络(Faster- rcnn)检测航空图像中各种类型房屋的有效性。在建立数据集并提取边界框信息后,使用预训练的ResNet50得到特征映射。全卷积区域建议网络(RPN)首先从特征映射中预测对象(在本例中为房屋)的边界和对象得分。然后,感兴趣区域(RoI)池化层提取感兴趣的区域来检测图像中存在的物体。据我们所知,这是第一次尝试使用更快的R-CNN来检测房屋,并取得了令人满意的结果。这一实验为民用和环境领域以及其他应用科学领域的研究开辟了一条新的道路。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
"Towards Real-time House Detection in Aerial Imagery Using Faster Region-based Convolutional Neural Network"
In the past few years, automatic building detection in aerial images has become an emerging field in computer vision. Detecting the specific types of houses will provide information in urbanization, change detection, and urban monitoring that play increasingly important roles in modern city planning and natural hazard preparedness. In this paper, we demonstrate the effectiveness of detecting various types of houses in aerial imagery using Faster Region-based Convolutional Neural Network (Faster-RCNN). After formulating the dataset and extracting bounding-box information, pre-trained ResNet50 is used to get the feature maps. The fully convolutional Region Proposal Network (RPN) first predicts the bounds and objectness score of objects (in this case house) from the feature maps. Then, the Region of Interest (RoI) pooling layer extracts interested regions to detect objects that are present in the images. To the best of our knowledge, this is the first attempt at detecting houses using Faster R-CNN that has achieved satisfactory results. This experiment opens a new path to conduct and extent the works not only for civil and environmental domain but also other applied science disciplines.
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来源期刊
IPSI BgD Transactions on Internet Research
IPSI BgD Transactions on Internet Research COMPUTER SCIENCE, INFORMATION SYSTEMS-
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