基于深度学习的自动跟踪相机框架的自动检测无人机用于检测电力线缺陷

Joon-Young Park, Seok-Tae Kim, Jae-Kyung Lee, Ji-Wan Ham, Ki‐Yong Oh
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引用次数: 2

摘要

传统的由人工操作的无人机检查不适合用于输电线路的检查,因为铁塔及其跨度太高、太宽,250米的视线无法进行检查。为此,韩电研究院开发了以铁塔的GPS坐标为基础,自动沿着预定路线飞行的新型巡检无人机系统,该系统利用高清摄像机和热成像摄像机拍摄输电线的视频。在这个系统中,带有摄像机的相机万向架仍然由操作员从很远的地方控制。然而,当无人机接近一个钢塔时,相机万向架往往无法被控制,万向架操作员的实时视频传输有时由于钢结构和通电电源导体的射频干扰而中断。为了解决这一领域的控制问题,我们还开发了一种基于深度学习的自动跟踪相机万向架,可以自动跟踪和拍摄电力设施。有了自动云台,整个检测过程可以完全自动化。通过现场试验,验证了开发的整体系统的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Automatic Inspection Drone with Deep Learning-based Auto-tracking Camera Gimbal to Detect Defects in Power Lines
The traditional drone inspection performed by human operators is unsuited for the purpose of inspecting power transmission lines, because steel towers and their spans are too high and wide to be inspected with a 250 m line of sight. For this reason, the KEPCO Research Institute developed a new inspection drone system that can automatically fly a predetermined flight path based on the GPS coordinates of steel towers, filming a video of power transmission lines with a high definition camera and a thermal imaging camera. In this system, a camera gimbal with the cameras was still controlled by a human operator from a long distance away. When the drone approached close to a steel tower, however, the camera gimbal was often unable to be controlled and real-time video transmission for the gimbal operator was sometimes interrupted due to radio-frequency interference from steel structure and energized power conductors. To solve such a control problem in the field, we also developed an auto-tracking camera gimbal that can automatically track and photograph power facilities on the basis of Deep Learning. With the automatic gimbal, the entire inspection process can be fully automated. The effectiveness of the developed overall system was confirmed through field tests.
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