| Issue |
E3S Web Conf.
Volume 257, 2021
5th International Workshop on Advances in Energy Science and Environment Engineering (AESEE 2021)
|
|
|---|---|---|
| Article Number | 01019 | |
| Number of page(s) | 4 | |
| Section | Energy Chemistry and Energy Storage and Save Technology | |
| DOI | https://doi.org/10.1051/e3sconf/202125701019 | |
| Published online | 12 May 2021 | |
Research on Image Recognition of Electrical Equipment based on Deconvolution Feature Extraction
1
North China Electric Power University, Department of Electrical Engineering, Lianchi, Baoding, Hebei, China
2
North China Electric Power University, Department of Electrical Engineering, Lianchi, Baoding, Hebei, China
* Corresponding author: m15561436123@163.com
Based on machine learning technology and combining the operation of machine learning from the idea of neural network, this paper focuses on the classification and recognition of image data of transformers, circuit breakers and isolation switches in substations. Firstly, the image enhancement is carried out on the basis of the original image, which simulates the possible scenes in reality. Secondly, using the dual-mode a deconvolutional network to capture significant features from in-depth visible and infrared images. Furthermore, all these features are subjected to the program to conduct transfer learning and weighted fusion. The dual-mode deconvolutional network (DMDN) extracts and highlights the features of the electrical equipment. Compared to traditional model, the recognition accuracy of the improved model is reached at 99.17%.
© The Authors, published by EDP Sciences, 2021
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