摘要
输电线路安全是电网安全稳定运行的前提,但是鸟类对输电线路造成的危害直接威胁到输电线路的安全运行.为解决传统驱鸟器启停策略的弊端,提出基于YOLO v3算法的输电线路鸟类检测模型.通过输电线路监控装置获取图像数据,使用残差模块提取图像的深层次特征,采用多尺度目标检测策略来保证鸟类的检测效果.实验结果表明,在输电线路鸟类检测任务中,该模型准确率可以达到86.75%,检测速度达到47 frame/s,可以精确实时地检测出输电线路周围的鸟类数目,并验证了该模型在雨天、雾天、抖动情况下具有较强鲁棒性,可以保障输电线路的安全、稳定运行.
The safety of electric transmission line is the prerequisite of a safe and stable operation of power grid,but the damage caused by birds directly threatens the safe operation of electric transmission line.To address the disadvantages of traditional bird repellent start-stop strategy,this paper proposes a bird detection model for electric transmission lines based on YOLO v3 algorithm.This model obtains the image data through electric transmission line monitoring device,extracts the deep features of images by residual module and uses multiple scale object detection strategy to guarantee the bird detection effect.Experimental results show that in the bird detection tasks for electric transmission line,the accuracy of the proposed model can reach 86.75%and the detection speed can be up to 47 frame/s.This model can accurately and timely detect the bird number around the electric transmission line and its high robustness is verified in rainy,foggy and jittering scenes,which proves it can guarantee a safe and stable operation of electric transmission line.
作者
陈咏秋
孙凌卿
张永泽
傅启明
陆宇
李渊博
孙建刚
CHEN Yongqiu;SUN Lingqing;ZHANG Yongze;FU Qiming;LU Yu;LI Yuanbo;SUN Jiangang(Jiangsu Electric Power Information Technology Co.,Ltd.,Nanjing 210000,China)
出处
《计算机工程》
CAS
CSCD
北大核心
2020年第4期294-300,共7页
Computer Engineering
基金
国家电网公司科技项目(XM201831160132)。
关键词
YOLO
v3算法
输电线路
鸟类检测
多尺度目标
实时检测
深度学习
YOLO v3 algorithm
electric transmission line
bird detection
multiple scale object
timely detection
deep learning
