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A Roadmap for Recognizing Engineering Vehicle from Aerial Images of UAV

机译:从UAV的空中图像识别工程车辆的路线图

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Engineering vehicles on construction sites mainly include excavators, wheeled cranes and so on. If the engineering vehicle is working under or near the high-voltage power line, its bucket or boom probably enter the high-voltage breakdown range when they are lifted, which is very easy to result in accidents such as short circuit breakdown. So, it is necessary to find out the engineering vehicles working near high-voltage power line during inspection. Unmanned aerial vehicle (UAV) inspection is one of the main methods of electric power inspection at present. Lots of images are produced by UAV during the power line inspection. It will save a lot of inspection work if the engineering vehicles working near high-voltage power line can be recognized from these images. First, this paper analyzes the specific requirements of engineering vehicle recognition from aerial images of UAV power line inspection. Then, based on the research status of vehicle recognition in aerial images and other related fields at domestic and abroad, this paper comprehensively analyzes the research status of classical pattern recognition method and deep neural network method to recognize engineering vehicles in aerial images of UAV. Third, in view of the practical problems such as the low aerial image data of engineering vehicles, the roadmap of recognizing the engineering vehicles in the aerial image of UAV using the capsule network method is designed.
机译:建筑工地的工程车辆主要包括挖掘机,轮式起重机等。如果工程车辆在高压电源线下工作或靠近高压电源线,其铲斗或臂可能会在抬起时进入高压击穿范围,这很容易导致诸如短路故障的事故。因此,有必要在检查期间找出在高压电力线附近工作的工程车辆。无人驾驶飞行器(UAV)检查是目前电力检测的主要方法之一。在电源线检查期间,通过UAV生产了很多图像。如果可以从这些图像识别在高压电源线附近的工程车辆,它将节省大量检查工作。首先,本文分析了从UAV电力线检查的空中图像的工程车辆识别的具体要求。然后,根据航空图像和国内外相关领域的车辆识别研究现状,本文全面分析了经典模式识别方法和深神经网络方法的研究现状,以识别无人机的空中图像工程车辆。三,鉴于工程车辆的低空中图像数据等实际问题,设计了使用胶囊网络方法识别UAV的空中图像中的工程车辆的路线图。

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