Mutual inductor nameplate text area detection method based on deep learning
A text area, deep learning technology, applied in the field of deep learning and image recognition, can solve the problems of error-prone, low efficiency and high cost
Pending Publication Date: 2021-09-10
BEIJING UNIV OF POSTS & TELECOMM
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At present, the identification and statistics of various information of transformers are done manually, which is inefficient, costly and error-prone
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[0037]The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0038] Such as figure 1 The above is a schematic diagram of the overall structure of the deep learning-based transformer nameplate text detection model of the embodiment of the application. The overall network structure consists of two parts: the model backbone network and the DB network pixel classifier. Among them, the backbone network adopts the network structure of U-Net to extract the features of each dimension of the input image, and perform feature fusion;...
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The invention discloses a mutual inductor nameplate text area detection method based on deep learning. According to the method, a first-stage model is used for detecting a text area on a nameplate of the transformer equipment by using an image pixel classification principle. The mutual inductor nameplate image feature extraction and fusion method adopts a U-Net network multi-dimensional feature fusion method, and features of character areas with different sizes in an image can be accurately extracted through the method. Meanwhile, in order to improve the recognition performance of the long text in the transformer nameplate image, a Difference Binarization (DB) network is adopted to associate, map and classify the fused features in the text detection stage, so that the situation that the long text with semantic association is cut off during text detection is avoided. Therefore, through a mode of combining the U-Net network and the DB network, the detection capability of the model on small-region texts is improved, and the feature learning capability of the model on long texts is also enhanced, so that the precision of the whole text detection model is improved.
Description
technical field [0001] The invention relates to the field of deep learning and image recognition, and is a deep learning-based method for detecting the text area of a nameplate of a transformer. Background technique [0002] Transformers are an important part of current power systems. With the continuous development of my country's power system, the demand for various transformers is also increasing. Therefore, in order to manage transformer equipment more scientifically, it is necessary to make reasonable statistics on various types and specifications of transformers. At present, the identification and statistics of various information of transformers are done manually, which is inefficient, costly and error-prone. Therefore, automatic identification and statistics of nameplate information of transformer equipment is an important research direction. In this process, the first thing to be solved is the detection of the text area of the transformer nameplate, which is ...
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IPC IPC(8): G06K9/32G06K9/34G06K9/46G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/045G06F18/241
Inventor 于秀丽董明帅魏世民吴澍白宇轩杨奉豪
Owner BEIJING UNIV OF POSTS & TELECOMM