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Development of Power Transmission Line Defects Diagnosis System for UAV Inspection based on Binocular Depth Imaging Technology

机译:基于双目深度成像技术的无人机检查输电线路缺陷诊断系统的开发

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China's transmission lines are widely distributed. The power department spends huge manpower and material resources every year on transmission line patrolling. The development of Unmanned Aerial Vehicle (UAV) technology provides a new inspection method for overhead transmission line patrolling. In the present days, most of the inspection images of drones are based on monocular cameras. Due to the limitations of two-dimensional images, this detection method is not sufficient. Considering the issue that defects of transmission lines are not ideal in the complex natural environment background, in this paper a deep imaging UAV inspection transmission line defect and lurking peril diagnosis system is proposed. This paper introduces the related theory of binocular stereo vision, and describes in detail related image processing techniques such as binocular camera calibration, stereo image correction, stereo matching, and image segmentation. By studying the imaging model of the binocular vision system, a set of automatic transmission line detection platform is established which greatly improve the efficiency of the inspection of the UAV transmission line. Using the system, real-time inspection of transmission lines, intelligent diagnosis of defects is performed which greatly improves the diagnosis efficiency and reduces the manually workload.
机译:中国的输电线路分布广泛。电力部门每年在输电线路巡逻上花费大量的人力和物力。无人机技术的发展为架空输电线路巡逻提供了一种新的检查方法。目前,大多数无人机检查图像都是基于单眼相机。由于二维图像的限制,这种检测方法是不够的。针对复杂自然环境条件下输电线路缺陷不理想的问题,提出了一种深度成像无人机巡检输电线路缺陷和潜伏危险的诊断系统。本文介绍了双目立体视觉的相关理论,并详细介绍了相关的图像处理技术,例如双目相机校准,立体图像校正,立体匹配和图像分割。通过研究双目视觉系统的成像模型,建立了一套自动传输线检测平台,大大提高了无人机传输线的检查效率。使用该系统,对传输线进行实时检查,对缺陷进行智能诊断,大大提高了诊断效率,减少了人工工作量。

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