文章基本信息

标题：Research on Image Recognition of Electrical Equipment based on Deconvolution Feature Extraction
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作者：Zhe Li ; Haifeng Su
期刊名称：E3S Web of Conferences
印刷版ISSN：2267-1242
电子版ISSN：2267-1242
出版年度：2021
卷号：257
页码：1-4
DOI：10.1051/e3sconf/202125701019
语种：English
出版社：EDP Sciences
摘要：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%.