基于SE-YOLOv5s的绝缘子检测

doi:10.11959/j.issn.2096-6652.202132

智能科学与技术学报 ›› 2021, Vol. 3 ›› Issue (3): 312-321.doi: 10.11959/j.issn.2096-6652.202132

• 专刊：目标智能检测与识别 • 上一篇下一篇

基于SE-YOLOv5s的绝缘子检测

田庆¹, 胡蓉¹, 李佐勇²^,³, 蔡远征²^,³, 余兆钗²^,³

¹ 福建工程学院计算机科学与数学学院，福建福州 350118
² 闽江学院计算机与控制工程学院，福建福州 350121
³ 福建省信息处理与智能控制重点实验室，福建福州 350121

修回日期:2021-07-27 出版日期:2021-09-15 发布日期:2021-09-01
作者简介:田庆（1998− ），男，福建工程学院计算机科学与数学学院硕士生，主要研究方向为深度学习、计算机视觉
胡蓉（1974− ），女，博士，福建工程学院计算机科学与数学学院教授，主要研究方向为人工智能、大数据、交通数据挖掘等
李佐勇（1980− ），男，博士，闽江学院计算机与控制工程学院教授，主要研究方向为计算机视觉、模式识别、深度学习等
蔡远征（1987− ），男，博士，闽江学院计算机与控制工程学院讲师，主要研究方向为计算机视觉与模式识别
余兆钗（1979− ），男，闽江学院计算机与控制工程学院讲师，主要研究方向为医学图像分析与模式识别
基金资助:
福建省自然科学基金资助项目(2020J02024);福建省自然科学基金资助项目(2020J01828);福州市科技计划资助项目(2020-RC-186)

Insulator detection based on SE-YOLOv5s

Qing TIAN¹, Rong HU¹, Zuoyong LI²^,³, Yuanzheng CAI²^,³, Zhaochai YU²^,³

¹ College of Computer Science and Mathematics, Fujian University of Technology, Fuzhou 350118, China
² College of Computer and Control Engineering, Minjiang University, Fuzhou 350121, China
³ Fujian Provincial Key Laboratory of Information Processing and Intelligent Control, Fuzhou 350121, China

Revised:2021-07-27 Online:2021-09-15 Published:2021-09-01
Supported by:
The Natural Science Foundation of Fujian Province(2020J02024);The Natural Science Foundation of Fujian Province(2020J01828);Fuzhou Science and Technology Project(2020-RC-186)

摘要/Abstract

摘要：

在电力系统需要巡检的大环境下，人工巡检的传统方式存在很大不便和安全隐患，而采用无人机的目标检测方法在绝缘子检测识别方向有很大的应用前景。针对绝缘子图像检测中存在的场景复杂、视角多变、设备计算能力受限等问题，提出了一种改进的轻量级SE-YOLOv5s卷积神经网络来实现对绝缘子的快速目标检测，该方法首先在YOLOv5s网络中融合SE注意力模块，以强化网络对绝缘子目标的辨识能力，随后采用K-means聚类方法构建绝缘子的先验框，以提升定位精度，最后构造置信度与定位任务联合的损失函数，并引入 Mosaic 数据增强策略训练网络，有效解决训练数据不足的问题。经过实验验证发现，与主流目标检测方法相比，提出的SE-YOLOv5s方法在绝缘子检测准确率、召回率、检测速度及平均精度均值等性能指标上均取得了较好的结果。实验结果表明，该网络对于绝缘子检测有很好的效果，具有更好的鲁棒性，对电力系统的巡检方式具有参考价值。

关键词: 绝缘子, 目标检测, 深度学习, YOLOv5s

Abstract:

In a large environment where the power system needs to be inspected, the traditional method of manual inspection has great inconvenience and potential safety risks, and the object detection method of unmanned aerial vehicle has great application prospects in the direction of insulator detection and recognition.SE-YOLOv5s, a lightweight insulator detection network that performs efficient detection for this task was presented.Firstly, backbone of YOLOv5s by fusing the SE attention module was strengthen.Then, the position distribution of insulator object was investigated and predefined templates of a priori box by K-means clustering on prior coordinate vectors were generated.Finally, the network by a multitask loss function was trained combined with confidence and position regression task.Furthermore, Mosaic data augmentation was utilized to supplement additional training samples.Experimental results demonstrate that the proposed SE-YOLOv5s significantly outperforms baseline methods at multiple criterions including accuracy, recall rate, detection rate and mean average precision.In comparison with the baselines, the proposed network has a flexible trade-off between robustness and memory overhead and it is a potential approach to promote the power system development.

Key words: insulator, object detection, deep learning, YOLOv5s

中图分类号:

田庆, 胡蓉, 李佐勇, 等. 基于SE-YOLOv5s的绝缘子检测[J]. 智能科学与技术学报, 2021, 3(3): 312-321.

Qing TIAN, Rong HU, Zuoyong LI, et al. Insulator detection based on SE-YOLOv5s[J]. Chinese Journal of Intelligent Science and Technology, 2021, 3(3): 312-321.

图/表 12

图1

图2

图3

图5

图4

图6

图7

表1

表2

图8

图9

图10

参考文献 19

[1]	付晶, 邵瑰玮, 吴亮 ,等. 利用层次模型进行训练学习的线路设备缺陷检测方法[J]. 高电压技术, 2017,43(1): 266-275.
	FU J , SHAO G W , WU L ,et al. Defect detection of line facility using hierarchical model with learning algorithm[J]. High Voltage Engineering, 2017,43(1): 266-275.
[2]	陆佳政, 谢鹏康, 方针 ,等. 重覆冰条件下防冰防雷绝缘子电场仿真与伞裙优化[J]. 电力自动化设备, 2018,38(3): 199-204.
	LU J Z , XIE P K , FANG Z ,et al. Electric field simulation and sheds optimization of anti-icing and anti-lightning insulator under heavy icing condition[J]. Electric Power Automation Equipment, 2018,38(3): 199-204.
[3]	阳武 . 基于航拍图像的绝缘子识别与状态检测方法研究[D]. 北京:华北电力大学, 2016.
	YANG W . Research on insulator recognition and state detection method based on aerial image[D]. Beijing:North China Electric Power University, 2016.
[4]	程海燕, 韩璞, 王迪 ,等. 一种电网巡检航拍图像中绝缘子定位方法[J]. 系统仿真学报, 2017,29(6): 1327-1336.
	CHENG H Y , HAN P , WANG D ,et al. Location method of insulators in power grid patrol aerial images[J]. Journal of System Simulation, 2017,29(6): 1327-1336.
[5]	REDDY M J B , CHANDRA B K , MOHANTA D K . A DOST based approach for the condition monitoring of 11 kV distribution line insulators[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2011,18(2): 588-595.
[6]	REDDY M J B , B K C , MOHANTA D K . Condition monitoring of 11 kV distribution system insulators incorporating complex imagery using combined DOST-SVM approach[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2013,20(2): 664-674.
[7]	OBERWEGER M , WENDEL A , BISCHOF H . Visual recognition and fault detection for power line insulators[C]// Proceedings of the 19th Computer Vision Winter Workshop. Krtiny:Czech Society for Cybernetics and Informatics, 2014: 81-88.
[8]	ZHAO Z B , LIU N , WANG L . Localization of multiple insulators by orientation angle detection and binary shape prior knowledge[J]. IEEE Transactions on Dielectrics and Electrical Insulation, 2015,22(6): 3421-3428.
[9]	彭向阳, 陈驰, 饶章权 ,等. 基于无人机多传感器数据采集的电力线路安全巡检及智能诊断[J]. 高电压技术, 2015,41(1): 159-166.
	PENG X Y , CHEN C , RAO Z Q ,et al. Safety inspection and intelligent diagnosis of transmission line based on unmanned helicopter of multi sensor data acquisition[J]. High Voltage Engineering, 2015,41(1): 159-166.
[10]	WU Q G , AN J B , LIN B . A texture segmentation algorithm based on PCA and global minimization active contour model for aerial insulator images[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012,5(5): 1509-1518.
[11]	WU Q G , AN J B . An active contour model based on texture distribution for extracting inhomogeneous insulators from aerial images[J]. IEEE Transactions on Geoscience and Remote Sensing, 2014,52(6): 3613-3626.
[12]	WANG W G , WANG Y F , HAN J ,et al. Recognition and drop-off detection of insulator based on aerial image[C]// Proceedings of the 2016 9th International Symposium on Computational Intelligence and Design. Piscataway:IEEE Press, 2016: 162-167.
[13]	VASWANI A , SHAZEER N , PARMAR N ,et al. Attention is all you need[C]// Proceedings of the 31st Conference on Neural Information Processing Systems.[S.l.:s.n.], 2017.
[14]	WOO S , PARK J , LEE J Y ,et al. CBAM:convolutional block attention module[M]. Computer vision – ECCV 2018. Cham: Springer International Publishing, 2018: 3-19.
[15]	LI W , ZHU X T , GONG S G . Harmonious attention network for person Re-identification[C]// Proceedings of the 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 2285-2294.
[16]	田思佳, 顾强, 胡蓉 ,等. 一种基于深度学习的机械臂分拣方法[J]. 智能科学与技术学报, 2020,2(3): 268-274.
	TIAN S J , GU Q , HU R ,et al. A robot sorting method based on deep learning[J]. Chinese Journal of Intelligent Science and Technology, 2020,2(3): 268-274.
[17]	REDMON J , DIVVALA S , GIRSHICK R ,et al. You only look once:unified,real-time object detection[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2016: 779-788.
[18]	HU J , SHEN L , SUN G . Squeeze-and-excitation networks[C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Piscataway:IEEE Press, 2018: 7132-7141.
[19]	WANG C Y , MARK LIAO H Y , WU Y H ,et al. CSPNet:a new backbone that can enhance learning capability of CNN[C]// Proceedings of the 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. Piscataway:IEEE Press, 2020: 1571-1580.

α	准确率	召回率
-	98.24%	99.46%
0.6	98.16%	99.17%
0.7	98.19%	99.28%
0.8	98.19%	99.32%
0.9	98.21%	99.43%

模型	mAP	平均检测时间/ms
Faster R-CNN	95.58%	163.7
SSD	95.22%	53.5
YOLOv3^[19]	97.02%	64.4
YOLOv5	98.24%	36.2
SE-YOLOv5s	98.46%	19.8

基于SE-YOLOv5s的绝缘子检测

Insulator detection based on SE-YOLOv5s

在线阅读

PDF下载

可视化

摘要/Abstract

引用本文

使用本文

图/表 12

参考文献 19

相关文章 15

Metrics

推荐阅读 0

[1]	黄哲, 王永才, 李德英. 3D目标检测方法研究综述[J]. 智能科学与技术学报, 2023, 5(1): 7-31.
[2]	顾宙瑜, 於跃成, 者甜甜. Rapider-YOLOX：高效的轻量级目标检测网络[J]. 智能科学与技术学报, 2023, 5(1): 92-103.
[3]	卢经纬, 程相, 王飞跃. 求解微分方程的人工智能与深度学习方法：现状及展望[J]. 智能科学与技术学报, 2022, 4(4): 461-476.
[4]	张俊, 许沛东, 陈思远, 高天露, 戴宇欣, 张科, 赵杭, 高杰迈, 白昱阳, 李金星, 张浩然, 李湘, 陈玖香. 物理-数据-知识混合驱动的人机混合增强智能系统管控方法[J]. 智能科学与技术学报, 2022, 4(4): 571-583.
[5]	陈妍, 罗雪琴, 梁伟, 谢永芳. 基于情感信息融合注意力机制的抑郁症识别[J]. 智能科学与技术学报, 2022, 4(4): 600-609.
[6]	栗仁武, 张凌霄, 高林, 李淳芃, 蒋浩. 基于点云的类级别物体姿态估计[J]. 智能科学与技术学报, 2022, 4(2): 246-254.
[7]	李琳辉, 周彬, 任威威, 连静. 行人轨迹预测方法综述[J]. 智能科学与技术学报, 2021, 3(4): 399-411.
[8]	李颖, 陈龙, 黄钊宏, 孙杨, 蔡国榕. 基于多尺度卷积神经网络特征融合的植株叶片检测技术[J]. 智能科学与技术学报, 2021, 3(3): 304-311.
[9]	刘文, 胡琨林, 李岩, 刘钊. 移动目标轨迹预测方法研究综述[J]. 智能科学与技术学报, 2021, 3(2): 149-160.
[10]	张阳, 胡月, 辛东嵘. 一种考虑时空关联的深度学习短时交通流预测方法[J]. 智能科学与技术学报, 2021, 3(2): 172-178.
[11]	刘栋军, 王宇涵, 凌文芬, 彭勇, 孔万增. 基于脑机协同智能的情绪识别[J]. 智能科学与技术学报, 2021, 3(1): 65-75.
[12]	赵亮, 谢志峰, 张坤鹏, 郑玉卿, 付园坤. 无线网络信号传输建模：一种区间二型模糊集成深度学习方法[J]. 智能科学与技术学报, 2020, 2(4): 401-411.
[13]	田思佳,顾强,胡蓉,李锐戈,何顶新. 一种基于深度学习的机械臂分拣方法[J]. 智能科学与技术学报, 2020, 2(3): 268-274.
[14]	王飞跃,曹东璞,魏庆来. 强化学习：迈向知行合一的智能机制与算法[J]. 智能科学与技术学报, 2020, 2(2): 101-106.
[15]	袁小锋,王雅琳,阳春华,桂卫华. 深度学习在流程工业过程数据建模中的应用[J]. 智能科学与技术学报, 2020, 2(2): 107-115.