Multi-Geometric Reasoning Network for Insulator Defect Detection of Electric Transmission Lines

Author:

Zhai YongjieORCID,Hu ZhedongORCID,Wang QianmingORCID,Yang QiangORCID,Yang KeORCID

Abstract

To address the challenges in the unmanned system-based intelligent inspection of electric transmission line insulators, this paper proposed a multi-geometric reasoning network (MGRN) to accurately detect insulator geometric defects based on aerial images with complex backgrounds and different scales. The spatial geometric reasoning sub-module (SGR) was developed to represent the spatial location relationship of defects. The appearance geometric reasoning sub-module (AGR) and the parallel feature transformation (PFT) sub-module were adopted to obtain the appearance geometric features from the real samples. These multi-geometric features can be fused with the original visual features to identify and locate the insulator defects. The proposed solution is assessed through experiments against the existing solutions and the numerical results indicate that it can significantly improve the detection accuracy of multiple insulator defects using the aerial images.

Funder

National Natural Science Foundation of China

Hebei Province Natural Science Foundation

Publisher

MDPI AG

Subject

Electrical and Electronic Engineering,Biochemistry,Instrumentation,Atomic and Molecular Physics, and Optics,Analytical Chemistry

Cited by 3 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Context-Enhanced Network with Spatial-Aware Graph for Smartphone Screen Defect Detection;Sensors;2024-05-26

2. Guardians of Grids: A Novel Method for Identifying High-Voltage Insulator Flaws;2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE);2024-02-22

3. Deep Power Vision Technology and Intelligent Vision Sensors;Sensors;2023-12-05

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