Improving Neural Network Detection Accuracy of Electric Power Bushings in Infrared Images by Hough Transform

Author:

Zhao Hongshan,Zhang ZeyanORCID

Abstract

To improve the neural network detection accuracy of the electric power bushings in infrared images, a modified algorithm based on the You Only Look Once version 2 (YOLOv2) network is proposed to achieve better recognition results. Specifically, YOLOv2 corresponds to a convolutional neural network (CNN), although its rotation invariance is poor, and some bounding boxes (BBs) exhibit certain deviations. To solve this problem, the standard Hough transform and image rotation are utilized to determine the optimal recognition angle for target detection, such that an optimal recognition effect of YOLOv2 on inclined objects (for example, bushing) is achieved. With respect to the problem that the BB is biased, the shape feature of the bushing is extracted by the Gap statistic algorithm, based on K-means clustering; thereafter, the sliding window (SW) is utilized to determine the optimal recognition area. Experimental verification indicates that the proposed rotating image method can improve the recognition effect, and the SW can further modify the BB. The accuracy of target detection increases to 97.33%, and the recall increases to 95%.

Publisher

MDPI AG

Subject

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

Cited by 18 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. Editorial;IEEE Electrical Insulation Magazine;2024-03

2. Review of Detection Methods for Typical Faults in Transformer Bushings*;IEEE Electrical Insulation Magazine;2024-03

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4. On a Fast Hough/Radon Transform as a Compact Summation Scheme over Digital Straight Line Segments;Mathematics;2023-07-29

5. Ellipse shape prior based anti-noise network for parathyroid detection;Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022);2023-06-27

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