建筑工地无人机图像中的模板检测

K. Jahr, A. Braun, A. Borrmann
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引用次数: 5

摘要

施工进度的监控是施工现场的一项重要任务,目前大部分是手工进行的。最近的图像处理技术为减少现场体力劳动提供了一种很有前途的方法。虽然卷积神经网络等现代机器学习算法已被证明在其他应用领域具有崇高的价值,但迄今为止,它们被CAE行业广泛忽视。在本文中,我们提出了一种策略,建立一个机器学习程序来检测建筑工地无人机照片上的建筑元素。在随后的案例研究中,我们使用了750张包含近10,000个模板元素的照片,在对单个目标图像进行分类时,我们的准确率达到了90%,在对多目标图像进行模板定位时,准确率达到了40%。智能方法识别和定位施工要素在现场。在本文的第一部分中,我们概述了当今建筑工地使用的图像分析技术的现状,然后进一步描述了所使用的方法。我们以概念验证和结果总结来结束本文。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Formwork detection in UAV pictures of construction sites
The monitoring of the construction progress is an essential task on construction sites, which nowadays is conducted mostly by hand. Recent image processing techniques provide a promising approach for reducing manual labor on site. While modern machine learning algorithms such as convolutional neural networks have proven to be of sublime value in other application fields, they are widely neglected by the CAE industry so far. In this paper, we propose a strategy to set up a machine learning routine to detect construction elements on UAV photographs of construction sites. In an accompanying case study using 750 photographs containing nearly 10.000 formwork elements, we reached accuracies of 90% when classifying single object images and 40% when locating formwork on multi-object images. telligence approach to recognize and locate construction elements on site. In the first part of the paper, we give an overview of the state of the art in image analysis as used on construction sites today, followed by a further description of the used methodology. We conclude the paper with a proof of concept and a summary of our results.
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