Transmission Line Insulator Recognition Based on Artificial Images Data Expansion

doi:10.16182/j.issn1004731x.joss.21-0646

Abstract

Abstract:

Deep learning method has developed rapidly in the field of computer vision, but relies on a large quantities of training data. In the task of transmission line insulator automatic detection, problems such as insufficient number of aerial insulator images and poor diversity affect the accuracy of insulator recognition. An artificial insulator images data expansion method is proposed. Artificial insulator images are created by modeling software, and a compensation network is constructed. The artificial images are compensated and optimized by compensation network, and the aerial insulator image data set is expanded by the compensated artificial insulator images. The insulator recognition experiments are carried out on several typical convolutional neural networks. The results show that the proposed method improves the accuracy of insulator identification by an average of 2.1%, and the network is relatively lightweight, which verifies the effectiveness and advantages of the proposed method.

Key words: artificial images, data expansion, insulator, guided backpropagation, convolutional neural network

CLC Number:

Yaru Wang, Kai Yang, Yongjie Zhai, Congbin Guo, Wenqing Zhao, Jie Su. Transmission Line Insulator Recognition Based on Artificial Images Data Expansion[J]. Journal of System Simulation, 2022, 34(11): 2337-2347.

Figures/Tables 10

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评价指标	方法	卷积神经网络
评价指标	方法	AlexNet	VGG11	VGG13	ResNet18	Inception_v3
准确率/%	不进行数据扩充	93.10	89.34	89.32	80.98	62.60
	未补偿的人工图像进行数据扩充	95.25	90.56	90.18	79.41	63.35
	基于CycleGAN的图像风格迁移数据扩充	95.03	90.96	90.08	77.70	63.31
	本文方法	96.40	91.02	91.26	82.44	64.29
AUC	不进行数据扩充	0.98	0.94	0.95	0.86	0.61
	未补偿的人工图像进行数据扩充	0.98	0.97	0.95	0.86	0.63
	基于CycleGAN的图像风格迁移数据扩充	0.99	0.98	0.96	0.83	0.62
	本文方法	0.99	0.99	0.98	0.90	0.62
迭代次数	不进行数据扩充	80	100	100	776	531
	未补偿的人工图像进行数据扩充	50	80	110	456	382
	基于CycleGAN的图像风格迁移数据扩充	75	130	130	918	687
	本文方法	70	90	115	526	443

方法	卷积神经网络
方法	AlexNet	VGG11	VGG13	ResNet18	Inception_v3
基于CycleGAN的图像风格迁移数据扩充	27.06 G
本文方法	1.34 G	14.99 G	22.40 G	3.63 G	11.43 G