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Article

A Method Based on Multi-Network Feature Fusion and Random Forest for Foreign Objects Detection on Transmission Lines

1
Department of Energy and Electrical Engineering, School of Information Engineering, Nanchang University, Nanchang 330031, China
2
State Grid Jiangxi Electric Power Research Institute, Nanchang 330096, China
3
School of Advanced Technology, Xi’an Jiaotong-Liverpool University, Suzhou 215028, China
*
Author to whom correspondence should be addressed.
Academic Editor: Vincent A. Cicirello
Received: 3 March 2022 / Revised: 28 April 2022 / Accepted: 13 May 2022 / Published: 14 May 2022
(This article belongs to the Special Issue Advanced Intelligent Imaging Technology Ⅲ)
Foreign objects such as kites, nests and balloons, etc., suspended on transmission lines may shorten the insulation distance and cause short-circuits between phases. A detection method for foreign objects on transmission lines is proposed, which combines multi-network feature fusion and random forest. Firstly, the foreign object image dataset of balloons, kites, nests and plastic was established. Then, the Otus binarization threshold segmentation and morphology processing were applied to extract the target region of the foreign object. The features of the target region were extracted by five types of convolutional neural networks (CNN): GoogLeNet, DenseNet-201, EfficientNet-B0, ResNet-101, AlexNet and then fused by concatenation fusion strategy. Furthermore, the fused features in different schemes were used to train and test random forest, meanwhile, the gradient-weighted class activation mapping (Grad-CAM) was used to visualize the decision region of each network, which can verify the effectiveness of the optimal feature fusion scheme. Simulation results indicate that the detection accuracy of the proposed method can reach 95.88%, whose performance is better than the model of a single network. This study provides references for detection of foreign objects suspended on transmission lines. View Full-Text
Keywords: transmission lines; foreign object detection; feature fusion; random forest; convolutional neural network transmission lines; foreign object detection; feature fusion; random forest; convolutional neural network
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MDPI and ACS Style

Yu, Y.; Qiu, Z.; Liao, H.; Wei, Z.; Zhu, X.; Zhou, Z. A Method Based on Multi-Network Feature Fusion and Random Forest for Foreign Objects Detection on Transmission Lines. Appl. Sci. 2022, 12, 4982. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104982

AMA Style

Yu Y, Qiu Z, Liao H, Wei Z, Zhu X, Zhou Z. A Method Based on Multi-Network Feature Fusion and Random Forest for Foreign Objects Detection on Transmission Lines. Applied Sciences. 2022; 12(10):4982. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104982

Chicago/Turabian Style

Yu, Yanzhen, Zhibin Qiu, Haoshuang Liao, Zixiang Wei, Xuan Zhu, and Zhibiao Zhou. 2022. "A Method Based on Multi-Network Feature Fusion and Random Forest for Foreign Objects Detection on Transmission Lines" Applied Sciences 12, no. 10: 4982. https://0-doi-org.brum.beds.ac.uk/10.3390/app12104982

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