首页> 外文会议>Proceedings of the 2017 IEEE International Conference on Applied System Innovation >Unmanned Aerial Vehicle for infrastructure inspection with image processing for quantification of measurement and formation of facade map
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Unmanned Aerial Vehicle for infrastructure inspection with image processing for quantification of measurement and formation of facade map

机译:用于基础设施检查的无人飞行器,具有图像处理功能,用于量化测量和立面图的形成

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The increasing age of infrastructure such as bridges or tall buildings requires a significant effort to be performed with respect to inspection such that damage being considered critical can be recognized early in advance and a respective infrastructure management can be performed. Many countries take the advantage of advance in Unmanned Aerial Vehicle technologies and applied for mostly aerial mapping and some infrastructure inspection to lower down the cost and increase the safety of inspection personnel. Most of the UAV system in the market produce just either photo or HD video, which does not contain any quantifiable reading for engineer to identify the damage level. Second, the image or video captured are not always perpendicular to the target surface, therefore distortion of the image could cause the inaccurate of the actual measurement on the photo/video. Third, current inspection method only done by few pictures or video, but do not contain “useful map” of the structure. This paper present a in house design autopilot system integrated with different sensors and optical camera together to perform a quantification of the image, also same time data fusion allow image correction to increase the accuracy of the measurement, and most importantly, the corrected images are used to form “facade map” of the building for engineers to inspect the building easily.
机译:基础设施(例如桥梁或高层建筑)的寿命日渐增加,需要进行大量的检查工作,以便可以提前及早识别出被认为是关键的损坏,并可以执行相应的基础设施管理。许多国家利用无人飞行器技术的先进优势,大部分用于航图绘制和一些基础设施检查,以降低成本并提高检查人员的安全性。市场上的大多数无人机系统仅产生照片或高清视频,其中不包含任何可量化的读数以供工程师识别损坏程度。其次,捕获的图像或视频并不总是垂直于目标表面,因此图像的失真可能会导致在照片/视频上的实际测量结果不准确。第三,目前的检查方法仅由很少的图片或视频完成,但不包含结构的“有用图”。本文提出了一种内部设计的自动驾驶仪系统,该系统与不同的传感器和光学相机集成在一起以对图像进行量化,同时数据融合还允许图像校正以提高测量精度,最重要的是,使用校正后的图像形成建筑物的“立面图”,以便工程师轻松检查建筑物。

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