Research on Recognition Method of Electrical Components Based on YOLO V3

The reliability of electrical components affects the stable operation of the power system. Electrical components inspection has long been important issues in the intelligent power system. The main problems of traditional recognition methods of electrical components are low detection accuracy and poo...

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Bibliographic Details
Published in:IEEE access 2019, Vol.7, p.157818-157829
Main Authors: Chen, Haipeng, He, Zhentao, Shi, Bowen, Zhong, Tie
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Component reliability
Datasets
Deep Learning
Electric components
Electric power systems
electrical components
Feature extraction
Image recognition
Image reconstruction
Image resolution
Inspection
Insulators
object detection
Real time
Recognition
SRCNN
Unmanned aerial vehicles
YOLO V3
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Summary:The reliability of electrical components affects the stable operation of the power system. Electrical components inspection has long been important issues in the intelligent power system. The main problems of traditional recognition methods of electrical components are low detection accuracy and poor real-time performance, which are challenging to extract necessary features from the inspection images. This paper proposes a way to detect the electrical components in the Unmanned Aerial Vehicle (UAV) inspection image based on You Only Look Once (YOLO) V3 algorithm. Due to some of the inspection images are not clear, which result in the reduction of the available dataset. On this basis, we adopt Super-Resolution Convolutional Neural Network (SRCNN) to realize super-resolution reconstruction on the blurred image, which achieves the expansion of the dataset. We compare the performance of the proposed method with other popular recognition methods. The results of experiment verify the effectiveness of the proposed method, and the technique reaches high recognition accuracy, good robustness, and strong real-time performance for UAV power inspection system.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2019.2950053