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

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 fores...

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Bibliographic Details
Published in:Applied sciences 2022-05, Vol.12 (10), p.4982
Main Authors: Yu, Yanzhen, Qiu, Zhibin, Liao, Haoshuang, Wei, Zixiang, Zhu, Xuan, Zhou, Zhibiao
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Balloons
convolutional neural network
Deep learning
feature fusion
foreign object detection
Insulation
Kites
Morphology
Nests
Neural networks
random forest
Short circuits
Transmission lines
Unmanned aerial vehicles
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app12104982