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Towards automatic power line detection for a UAV surveillance system using pulse coupled neural filter and an improved Hough transform

机译:利用脉冲耦合神经滤波器和改进的Hough变换实现无人机监视系统的自动电力线检测

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摘要

Spatial information captured from optical remote sensors on board unmanned aerial vehicles (UAVs) has great potential in automatic surveillance of electrical infrastructure. For an automatic vision-based power line inspection system, detecting power lines from a cluttered background is one of the most important and challenging tasks. In this paper, a novel method is proposed, specifically for power line detection from aerial images. A pulse coupled neural filterudis developed to remove background noise and generate anudedge map prior to the Hough transform being employed touddetect straight lines. An improved Hough transform is usedudby performing knowledge-based line clustering in Houghudspace to refine the detection results. The experiment on real image data captured from a UAV platform demonstrates that the proposed approach is effective for automatic power line detection.
机译:从无人机上的光学遥感器捕获的空间信息在自动监控电气基础设施方面具有巨大潜力。对于基于视觉的自动电力线检查系统,从凌乱的背景中检测电力线是最重要且最具挑战性的任务之一。在本文中,提出了一种新的方法,专门用于从空中图像中检测电力线。在使用霍夫变换对直线进行检测之前,开发了脉冲耦合神经滤波器以消除背景噪声并生成边缘图。通过在Hough udspace中执行基于知识的线聚类,可以使用改进的Hough变换来细化检测结果。对从无人机平台捕获的真实图像数据进行的实验表明,该方法对于电力线自动检测是有效的。

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