Research on insulator detection technology based on end-to-end algorithm

doi:10.3969/j.issn.1674-1951.2021.02.005

Abstract

Abstract:

Insulators are important electrical components in power systems, so it is important to study the target detection of insulators. Traditional recognition methods are of low utilization rate of image information and low accuracy. With the development of deep learning, good recognition results have been achieved in image identification and image detection. End-to-end deep learning target detection methods （YOLOv1,SSD,YOLOv2）are used in testing a custom dataset of an insulator and the results are compared. The experimental results show that the end-to-end deep learning algorithm can identify and locate the insulator. Maintaining the current detection performance, the method can improve the detection speed for insulators and meet the requirement of real-time power inspection.

Key words: insulator detection, image identification, image detection, deep learning, dataset, end-to-end detection algorithm, target detection algorithm

CLC Number:

TP399：TM755

XIAO Xinshuai, TIAN Xiuxia, XU Man. Research on insulator detection technology based on end-to-end algorithm[J]. Huadian Technology, 2021, 43(2): 28-33.

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检测框架	训练日期	mAP	F_s
YOLOv1	2007年	63.4	45
SSD	2007年	74.3	46
YOLOv2	2007年	78.6	40

检测框架	检测时间/s	阈值（I_U）
YOLOv1	1.376	0.36
SSD	1.786	0.65
YOLOv2	2.234	0.82