基于Faster R-CNN的电网线路常见异物检测

doi:10.16628/j.cnki.2095-8188.2021.01.010

电器与能效管理技术 ›› 2021, Vol. 0 ›› Issue (1): 58-63.doi: 10.16628/j.cnki.2095-8188.2021.01.010

基于Faster R-CNN的电网线路常见异物检测

朱洪志¹, 孙震¹, 兰巧倩¹, 沈亮熠¹, 张冲², 贺兴²

1.国网上海市电力公司嘉定供电公司, 上海 201800
2.上海交通大学, 上海 201100

收稿日期:2020-04-28 出版日期:2021-01-30 发布日期:2021-02-05
作者简介:朱洪志(1991—),男,工程师,主要从事输配电线路运行与维护。|孙震(1988—),男,工程师,主要从事输配电线路运行管理和状态维修。|兰巧倩(1990—),女,工程师,主要从事输配电设备信息管理。
基金资助:
* 国家自然科学基金青年科学基金项目(51907121)

Detection of Common Foreign Matters on Power Grid Lines Based on Faster R-CNN

ZHU Hongzhi¹, SUN Zhen¹, LAN Qiaoqian¹, SHEN Liangyi¹, ZHANG Chong², HE Xing²

1. State Grid Shanghai Electric Power Company Jiading Power Supply Company,Shanghai 201800, China
2. Shanghai Jiao Tong University, Shanghai 201100, China

Received:2020-04-28 Online:2021-01-30 Published:2021-02-05

摘要/Abstract

摘要：

电网线路和设备上的异物搭挂缠绕容易造成线路间接触短路,严重威胁着电网的安全稳定运行。针对电网常见异物风筝、气球、鸟窝等,提出了一种基于Faster R-CNN的高精度目标检测及分类模型。针对某类型异物图片样本量不足的问题,设计数据增强方法有效扩充训练集数据,并对样本图片进行多尺度处理以提升模型的训练速度以及模型对不同尺度的适应性,所得模型在测试集上的准确率和召回率可分别达到93.12%、94.75%,提出经数据增强后的训练集训练出的基于Faster R-CNN的目标检测模型取得了较好的检测效果。

关键词: 电网异物, 卷积神经网络, 数据增强, 目标检测

Abstract:

Foreign matters hanging on the power grid line and electrical equipment are easy to cause the contact short circuit between the lines which threatens the safety and stability of the power grid.In this paper,a high-precision object detection model based on Faster R-CNN was proposed for common foreign matters in power grid,such as kites,balloons,birds’ nests.Aiming at the problem of insufficient sample size of pictures,the data augmentation method is designed to effectively expand the data of the training set.Multi-scale processing of sample images is also carried out to improve the training speed of the model and the adaptability of the model to different scales.The accuracy and recall rate of the model can reach 93.12% and 94.75% respectively.It is proved that the target detection model based on Faster R-CNN trained by the training set with data enhancement has achieved better detection results.

Key words: foreign matters, convolutional neural network, data augmentation, object detection

中图分类号:

TM930

朱洪志, 孙震, 兰巧倩, 沈亮熠, 张冲, 贺兴. 基于Faster R-CNN的电网线路常见异物检测[J]. 电器与能效管理技术, 2021, 0(1): 58-63.

ZHU Hongzhi, SUN Zhen, LAN Qiaoqian, SHEN Liangyi, ZHANG Chong, HE Xing. Detection of Common Foreign Matters on Power Grid Lines Based on Faster R-CNN[J]. LOW VOLTAGE APPARATUS, 2021, 0(1): 58-63.

图/表 11

图1

图2

表1

图3

图4

图5

图6

图7

图8

表2

表3

参考文献 16

[1]	贺兴, 艾芊, 邱才明, 等. 泛在电力物联网数据挖掘体系建设综述及数据驱动认知框架探究[J]. 电器与能效管理技术, 2019(19):1-4.
[2]	张培铭. 智能低压电器技术研究[J]. 电器与能效管理技术, 2019(15):10-20.
[3]	GIRSHICK R, DONAHUE J, DARRELL T, et al. Rich feature hierarchies for accurate object detection and semantic segmentation[C]. CVPR,IEEE, 2014.
[4]	GIRSHICK R. Fast R-CNN[C]// 2015 IEEE International Conference on Computer Vision, 2015:1440-1448.
[5]	REN S Q, HE K M, GIRSHICK R, et al. Faster R-CNN:towards real-time object detection with region proposal networks[J]. IEEE Transactions on Pattern Analysis & Machine Intelligence, 2015,39(6):1137-1149. doi: 10.1109/TPAMI.2016.2577031 pmid: 27295650
[6]	雷忠腾, 宋杰. 基于卷积神经网络模型Faster R-CNN的遥感影像目标识别研究[J]. 测绘与空间地理信息, 2021,44(1):149-151,155.
[7]	刘媛. 基于深度学习的遥感图像目标检测方法[J]. 科学技术创新, 2021(3):84-85.
[8]	REDMON J, DIVVALA S, GIRSHICK R, et al. You only look once:Unified,real-time object detection. Computer Vision & Pattern Recognition[C]. 2016.
[9]	LIU W, ANGUELOV D, ERHAN D, et al. SSD:single shot multiBox detector:European Conference on Computer Vision. Springer International Publishing[C]. 2016.
[10]	汤踊, 韩军, 魏文力, 等. 深度学习在输电线路中部件识别与缺陷检测的研究[J]. 电子测量技术, 2018,41(6):60-65.
[11]	林刚, 王波, 彭辉, 等. 基于改进Faster-RCNN的输电线巡检图像多目标检测及定位[J]. 电力自动化设备, 39(5):213-218.
[12]	虢韬, 杨恒, 时磊, 等. 基于Faster RCNN的绝缘子自爆缺陷识别[J]. 电瓷避雷器, 2019(3):183-189.
[13]	SHIN H C, ROTH H R, GAO M, et al. Deep convolutional neural networks for computer-aided detection:CNN architectures,dataset characteristics and transfer learning[J]. IEEE Transactions on Medical Imaging, 2016,35(5):1285-1298. doi: 10.1109/TMI.2016.2528162 pmid: 26886976
[14]	盛恒, 黄铭, 杨晶晶. 基于Faster R-CNN和IoU优化的实验室人数统计与管理系统[J]. 计算机应用, 2019,39(6):1669-1674.
[15]	BALLESTER P L, ARAUJO R M. On the performance of GoogLeNet and AlexNet applied to sketches[C]// AAAI.AAAI Press, 2016.
[16]	张烨, 许艇, 冯定忠, 等. 基于难分样本挖掘的快速区域卷积神经网络目标检测研究[J]. 电子与信息学报, 2019,41(6):1496-1502.

旋转角度/(°)	亮度	色彩饱和度
30°	原图像×0.5	原图像×0.5
150°	原图像×1.5	原图像×1.5

目录	TP	FP	FN	准确率/%	召回率/%
扩充前	102	22	18	82.26	85.00
扩充后	379	28	21	93.12	94.75

算法	准确率/%	召回率/%	速度/(ms·张^-1)
基于Faster R-CNN	93.12	94.75	145
基于YOLOv3	82.46	85.74	93

基于Faster R-CNN的电网线路常见异物检测

Detection of Common Foreign Matters on Power Grid Lines Based on Faster R-CNN

RichHTML

PDF (PC)

可视化

摘要/Abstract

引用本文

使用本文

图/表 11

参考文献 16

相关文章 1

编辑推荐

Metrics

本文评价