House type graph recognition method and device based on neural network, equipment and storage medium

Abstract

The invention relates to the field of image detection, and provides a house type image recognition method and device based on a neural network, equipment and a storage medium, and the method comprisesthe steps: obtaining a required house type image when receiving a house type image recognition instruction sent by a query terminal, and carrying out the feature extraction of the required house typeimage based on a preset composite network model, and obtaining a required feature vector; performing house type graph query according to the demand feature vector to judge whether a matched target house type graph exists in a preset house type graph library or not; if yes, returning the target house type image to the query terminal; and if not, returning a corresponding house type graph non-existence prompt to the query terminal. According to the invention, the characteristics of the house type image are extracted through the composite network model, and the house type image is detected and matched through the extracted characteristics, so that the intelligent identification of the house type image is realized, the identification efficiency is improved, the manual workload is reduced, andthe labor cost is saved.

Description

Technical field [0001] The present invention relates to the field of image detection, in particular to a method, device, equipment and storage medium for recognizing a house pattern based on a neural network. Background technique [0002] The floor plan is the plane space layout of the house, that is, the diagram describing the use function, corresponding location and size of each independent space. In the real estate field, the layout of the house can be seen intuitively through the house map; in this regard, real estate companies often collect a large number of house plans and provide users with house map identification and matching services. However, when faced with massive house plan data, it is inefficient to manually identify and find the same house plan, and because of the limited amount of personal memory, it is impossible to remember the massive house plan, so it is difficult to identify whether a new house plan is already in the picture library. There is a matching flo...

AI Technical Summary

Problems solved by technology

However, in the face of massive house type map data, it is inefficient to manually identify and search for the same house type map, and due to limited personal memory, it is impossible to memorize massive house type maps, so it is not easy to identify whether a new house type map is already in the image library. There is a matching floor plan

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