Transmission Line Insulator Identification Based on Improved Libra-RCNN

doi:10.3969/j.issn.1008-0198.2022.02.008

Hunan Electric Power ›› 2022, Vol. 42 ›› Issue (2): 44-49.doi: 10.3969/j.issn.1008-0198.2022.02.008

• New technology and Application • Previous Articles Next Articles

Transmission Line Insulator Identification Based on Improved Libra-RCNN

LYU Haiqing¹, LEI Yuanhua¹, WANG Jing², XING Xuemin³, YANG Jing⁴

1. China Energy Engineering Group Hunan Electric Power Design Institute Co., Ltd., Changsha 410007, China;
2. Hunan Third Surveying and Mapping Institute, Changsha 410004, China;
3. School of Traffic and Transportation Engineering, Changsha University of Science & Technology, Changsha 410114, China;
4. China Water Resources and Hydropower Eighth Engineering Bureau Co., Ltd., Changsha 410004, China

Received:2021-11-30 Revised:2022-02-28 Published:2025-08-05

Abstract

Abstract: To solve the problems of poor positioning accuracy and stability of existing insulators identified by UAV aerial photography transmission lines, a Libra-RCNN insulator detection model based on ASFF pyramid network is proposed. Firstly, the FRN normalized layer is used to replace the original BN layer to eliminate the dependence of the normalized layer on the size of training batch and increase the learning efficiency of the model.Then, ASFF network structure is introduced into Libra-RCNN algorithm pyramid to effectively solve the problem of inconsistency inside the feature pyramid.Finally, GIoU crossover ratio is used to replace the original IoU crossover ratio to better accurate insulator position.In Insulators_Datasets data set, the average accuracy of improved Libra-RCNN model is 94.10%, and the recall rate is 97.51%.Compared with the original Libra-RCNN model, the improvement rate is 2.23% and 2.61% respectively, which indicates that the proposed algorithm can identify insulators stably and effectively.

Key words: insulator detection, Libra-RCNN model, filter response normalization(FRN) layer, adaptively spatial feature fusion(ASFF)network, generalized intersection over union

CLC Number:

TP391
TM854

LYU Haiqing, LEI Yuanhua, WANG Jing, XING Xuemin, YANG Jing. Transmission Line Insulator Identification Based on Improved Libra-RCNN[J]. Hunan Electric Power, 2022, 42(2): 44-49.

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Transmission Line Insulator Identification Based on Improved Libra-RCNN

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