摘要
鸟害是威胁我国输电线路安全稳定运行的重要因素之一。近年来中国架空输电线路发生鸟害故障的次数呈逐年增加的趋势,据此本文提出了一种鸟巢识别中的无人机优化巡检准则。本文主要采用Hough算法提取无人机巡检图像特征以识别杆塔;在杆塔识别区,提取颜色纹理特征以识别鸟巢。本文针对无人机巡检鸟巢漏检问题,利用三维建模软件SolidWorks,建立伞型高压杆塔与鸟巢的三维仿真模型,从而提炼出鸟巢识别中的一种无人机优化拍摄准则。该准则能有效降低杆塔遮挡对鸟巢检测的干扰影响,从而达到提高鸟巢检测灵敏性的目的。
Bird damage is one of the critical factors which threaten the stability of China’s electric transmission lines. Analyzing the increasing frequency of transmission malfunction owing to birds in recent years, this article is propounding an optimizing principle for UAV (unmanned aerial vehicles) inspection in bird nest detection. For identifying towers, Hough arithmetic is adopted to extract features from UAV images. In these tower-identified areas, through extracting color and texture features, bird nest is recognizable. Moreover, in allusion to inspection omission, SolidWorks contributes to constructing three-dimensional simulation models of umbrella-type high-tension towers and bird nests. Therefore, a type of UAV shooting rule is refined to detect nests, which is capable of effectively lowering towers’ disruption to test bird nests, and consequently, boosting detection sensitivity.
出处
《人工智能与机器人研究》
2020年第2期110-122,共13页
Artificial Intelligence and Robotics Research
基金
广东省2020年科技创新战略专项资金(“攀登计划”专项资金)“基于优化深度卷积神经网络的图像组合识别智能控制系统”(项目编号:pdjh2020b0182)。
