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Image recognition method based on deep learning

An image recognition and deep learning technology, which is applied in the field of image recognition based on deep learning, can solve problems such as poor scalability, low accuracy of the recognition result of the floor plan, and complex calculation of the recognition process.

Pending Publication Date: 2020-10-20
北京比邻弘科科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides an image recognition method based on deep learning, which solves the problems in the prior art that the recognition result of the house type map is not high in accuracy, the recognition process is complex in calculation and has poor scalability.

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Embodiment 1

[0057] Such as figure 1 as shown, figure 1 A flow chart of an image recognition method based on deep learning provided by an embodiment of the present invention is shown. The execution subject of the method of this embodiment is any electronic device with computing and processing capabilities. The method specifically includes the following steps:

[0058] S1. Obtain the corner point information and character information of the floor plan to be recognized, and input the corner point information into the quadratic programming model to obtain the connection information of wall lines and doors and windows.

[0059] For example, the connection information may include a connection line constituting two end points of a wall line, a connection line between two end points constituting a door or window icon.

[0060] The quadratic programming model is a pre-established model including a plurality of constraints corresponding to corner point connection rules and positional relationships...

Embodiment 2

[0109] Such as Figure 4 As shown, the deep learning-based image recognition method of this embodiment may include the following steps:

[0110] Step 01: Input the two-dimensional floor plan to be recognized into the trained neural network, and then obtain corner information.

[0111] It should be noted that the trained neural network can be image 3 The neural network obtained by training in .

[0112] Step 02: Input the corner point information output by the neural network into the quadratic programming model to obtain the connection information model between the corner points.

[0113] The quadratic programming model in this embodiment may be a model including multiple constraint conditions.

[0114] Step 03: Input the connection information into the graph theory optimization model to obtain the connected area information and the area composition information corresponding to the door and window information, that is, the area composition information of the room.

[0115]...

Embodiment 3

[0139] in addition, Figure 5 It is a schematic structural diagram of an electronic device provided by an embodiment of the present invention. The mobile device may be a mobile terminal, an IPAD, a computer, and the like. the above figure 1 and Figure 4 The described methods may be implemented by electronic devices.

[0140] Figure 5 The mobile device shown may include at least one processor 61 , at least one memory 62 , at least one network interface 64 and other user interfaces 63 . Various components in the mobile device are coupled together via a bus system 65 . It can be understood that the bus system 65 is used to realize connection and communication between these components. In addition to the data bus, the bus system 65 also includes a power bus, a control bus and a status signal bus. But for clarity, in Figure 5 The various buses are identified as bus system 65 in FIG.

[0141] Wherein, the user interface 63 may include a display, a keyboard, or a pointing...

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Abstract

The invention relates to an image recognition method based on deep learning, and the method comprises the steps: S1, obtaining the corner information and character information of a to-be-recognized house type image, inputting the corner information into a quadratic programming model, and obtaining the connection information of wall lines and doors and windows, wherein the quadratic programming model is pre-established and comprises a plurality of corresponding corner point connection rules and a model of constraint conditions of a position relationship between a wall line and a door and window; S2, inputting the connection information of the wall lines and the doors and windows into a graph theory optimization model, and obtaining region composition information of each room region in the house type graph, wherein the graph theory optimization model is a Graham algorithm model which is established in advance and is improved based on graph theory knowledge; and S3, matching the characterinformation with the region composition information to obtain an recognition result. The problems that in the prior art, the house type image recognition result precision is not high, the recognitionprocess calculation is complex, and the expansibility is poor are solved.

Description

technical field [0001] The present invention relates to the technical field of image processing, in particular to an image recognition method based on deep learning. Background technique [0002] In the field of smart home decoration design, with the rapid development of Internet technology and artificial intelligence technology, the demand for various online experiences has surged. No matter in terms of automatic layout or 3D reconstruction, the automatic extraction of floor plan elements has important practical significance. The understanding of the information in the building floor plan can accurately express the geometric and semantic information of the real scene. These geometric and semantic information usually include: the area information of the room, the position of doors and windows, and the geometric arrangement information of objects; The recognition of geometric and semantic information in the network has become a hot spot in current research. [0003] The info...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06N3/08
CPCG06N3/08G06V20/20G06V30/153G06V30/10
Inventor 陈文栋刘川
Owner 北京比邻弘科科技有限公司
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