基于优化双目视觉识别方案的输电线路监控研究
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摘要
计算机视觉技术作为一项实用技术广泛应用于各大领域,特别是现在仍需依靠大量人力监控的电网输电线路障碍物预警领域。本文利用改进的背景差分法对危险异物进行识别,同时利用优化的双目视觉识别方案对输电线路与异物进行三维重建与判断,并通过实验室环境对所提出的方案进行验证,从而证明优化方案的可行性,为输电线路监控提供更易实现的计算机视觉方案。
As a practical technology, computer vision technology is widely used in various fields, especially in the field of power grid transmission line obstacle warning which still relies on a lot of human monitoring. In this paper, an improved background difference method is used to identify dangerous foreign objects. At the same time, the optimized binocular visual recognition scheme is used to reconstruct and identify the transmission line and foreign objects in three dimensions, and the proposed scheme is verified by tests in the laboratory environment. The feasibility of the proposed scheme is proved, which provides a more achievable computer vision solution for transmission line monitoring.
As a practical technology, computer vision technology is widely used in various fields, especially in the field of power grid transmission line obstacle warning which still relies on a lot of human monitoring. In this paper, an improved background difference method is used to identify dangerous foreign objects. At the same time, the optimized binocular visual recognition scheme is used to reconstruct and identify the transmission line and foreign objects in three dimensions, and the proposed scheme is verified by tests in the laboratory environment. The feasibility of the proposed scheme is proved, which provides a more achievable computer vision solution for transmission line monitoring.
引文
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[2]李小平.山火预警技术在输电线路的应用现状[J].通讯世界,2016(24):142-143.
[3]赵连军.树木砍伐费对架空输电线路工程造价的影响[J].吉林电力,2006(5):21-23.
[4]刘贞瑶,谈发力,康宇斌,等.输电线路树木故障机理分析及试验研究[J].电力工程技术,2017,36(6):122-126.
[5]赵文清,沈哲吉,李刚.基于深度学习的用户异常用电模式检测[J].电力自动化设备,2018,38(9):34-38.
[6]张慧,王坤峰,王飞跃.深度学习在目标视觉检测中的应用进展与展望[J].自动化学报,2017,43(8):1289-1305.
[7]高林,王昌宇. 改进三帧差分法与背景差分法结合的运动目标检测算法[J]. 装备制造技术,2018,(3):172-177.
[8]王晓华.基于双目视觉的三维重建技术研究[D].青岛:山东科技大学,2004.
[9]郭玲.智能视频监控中运动目标检测的算法研究[D].广州:华南理工大学,2013.
[10]朱芳芳. 单目视觉定位中的模板检测技术研究[D].南京:南京航空航天大学,2009.
[11]靳盼盼.双目立体视觉测距技术研究[D].西安:长安大学,2014.
[12]魏菊锦.基于双目视觉的三维重建方法研究[D].沈阳:沈阳工业大学,2016.
[13]杜宇.三维重建中双目立体视觉关键技术的研究[D].哈尔滨:哈尔滨理工大学,2014.