CN113887508A - Method for accurately identifying center line of public corridor space in building professional residential plan - Google Patents
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Abstract
The invention belongs to the technical field of drawing analysis and discloses a method for accurately identifying a center line of a public corridor space in a building professional house plan, which comprises the following specific operation steps: s1, analyzing the CAD drawing to obtain basic information such as a primitive and a layer in the drawing; and S2, recommending the graph layer to the recommended graph layer of the component according to the basic information acquired in the step 1. The accurate identification method for the center line of the public corridor space in the CAD building professional house plan can accurately and stably obtain the center lines of all the public corridor spaces in the house plan, thereby providing good conditions for the regular examination of the public corridor in the house plan, facilitating the operation of workers, improving the working efficiency of the workers and being an identification method with good generalization performance and high accuracy.
Description
Technical Field
The invention belongs to the technical field of drawing analysis, and particularly relates to a method for accurately identifying a central line of a public corridor space in a building professional residential plan.
Background
The CAD construction drawing is a drawing which is made by using design software such as AutoCAD and the like to carry out overall layout of engineering projects, external shape, internal arrangement, structural construction, internal and external decoration, material making method, equipment, construction and the like of a building, has the characteristics of complete drawing, accurate expression and specific requirements, is a basis for carrying out engineering construction, construction drawing budget and construction organization design, is an important technical document for technical management, can enter a construction stage by carefully examining the construction drawing before construction, aims to ensure the smooth construction, and can avoid the influence on a use stage after construction due to the error of the drawing.
The building professional house plane map in the CAD construction drawing is a drawing composed of a horizontal projection method and a corresponding legend according to the building conditions of walls, doors and windows, stairs, ground, internal functional layout and the like of a new building or a structure, particularly, an imaginary horizontal cutting plane is used for cutting a house along a position slightly higher than a windowsill, then the upper part is removed, the rest part is projected to the H surface in a positive way, the obtained horizontal sectional view is obtained, the building plane map is used as an important component in the building design and construction drawing, reflects the functional requirements of the building, the plane layout and the plane composition relation thereof, is a key link for determining the vertical surface and the internal structure of the building, mainly reflects the conditions of the plane shape, the size, the internal layout, the specific positions of the ground, the doors and windows, the floor area and the like of the building, so the building plane map is an important basis for the construction and the site construction layout of the new building, the building house plane drawing method is also a basis for designing and planning professional engineering plane drawings of water supply and drainage, strong and weak electricity, heating and ventilation equipment and the like and drawing a comprehensive pipeline drawing diagram, the building house plane drawing is divided into a first floor plane drawing, a standard floor plane drawing, a top floor plane drawing, a roof plane drawing and the like, the first floor plane drawing of the building house represents the arrangement of rooms in a first floor, building entrances, hallways, stairs and the like, the standard floor plane drawing of the building house represents the arrangement of middle floors, the top floor plane drawing of the building house represents the plane arrangement drawing of the highest floor of a house, and the roof plane drawing of the building house represents the horizontal projection of a roof plane.
With the wide application of artificial intelligence, some work finished manually can be finished by the artificial intelligence, the examination of the CAD construction drawing is repetitive work which is time-consuming and labor-consuming, and examiners are easy to overlook, the artificial intelligence can identify the public corridor space in the CAD construction drawing, and the accurate identification of the center line of the public corridor space can be realized by means of a computer vision technology and a traditional image processing algorithm, so that the automatic examination of the corridor space, such as the examination of the corridor length, is realized.
Disclosure of Invention
The invention aims to provide a method for accurately identifying a central line of a public corridor space in a building professional house plan so as to solve the problems in the background technology.
In order to achieve the above purpose, the invention provides the following technical scheme: a method for accurately identifying a center line of a public corridor space in a building professional house plan comprises the following specific operation steps:
s1, analyzing the CAD drawing to obtain basic information such as a primitive and a layer in the drawing;
s2, recommending the graph layer to a recommended graph layer of the component according to the basic information acquired in the step 1;
s3, acquiring related information such as wall columns, door and window layers and the like from the recommended layer in the step 2;
s4, combining the primitive of the layer of the door and window in the step 3 to obtain a door and window component bbox;
s5, according to all the door and window members bbox obtained in the step 4, digging out small drawings of all doors and windows from the base drawings printed by the drawing;
s6, sending the small graph of the door and window member obtained in the step 5 into a MobileNet V1 deep convolution neural network for classification, and obtaining the accurate category of the door and window member after classification;
s7, printing the wall column graphic primitive in the step 3 on a base map, combining the door and window member information in the step 6, performing space segmentation by using image processing, and acquiring the function of each space;
s8, acquiring a public walkway space from all the spaces in the step 7, and then matting out a small picture of the space;
s9, zooming the small graph in the step 8 by utilizing an image processing technology;
s10, transmitting the zoomed small image in the step 9 into a Zhang-Suen thinning algorithm to obtain a central line of a corridor space;
s11, zooming the center line obtained in the step 10 to further obtain the center line of the corridor space in the original image;
s12, extending the central line obtained in the step 11 to further obtain an end point of the corridor space;
and S13, storing the central line obtained in the step 11 and the space end point of the corridor obtained in the step 12.
Preferably, the primitive in step S1 refers to graphics data, and corresponds to an entity visible on the drawing interface; the image layer refers to a film containing elements such as characters or figures, the film is stacked together in sequence and combined to form the final effect of the page, and the image layer can accurately position the elements on the page; texts, pictures, tables and plug-ins can be added into the layers, and the layers can be nested in the layers.
Preferably, the component is an exchangeable part actually present in the system, which implements a specific function, conforms to a set of interface standards and implements a set of interfaces, the component representing a physical implementation of a part of the system, including software code or its equivalent, and in the figures, the component is represented as a rectangle with a label.
Preferably, bbox in step S6 is a regression frame, and a predicted frame value is input at first, and the original classification problem is input as a single map, but the input map also includes position information of the frame in the original map; the input graph can extract a feature vector through convolutional network learning; one goal of target detection is to expect the last bounding box to coincide with the ground route.
Preferably, Mobile Net V1 in step S6 refers to the network architecture promulgated by Google, and the main innovation is to replace the normal convolution with a deep separable convolution and use a width multiplier to reduce the number of parameters, which can trade off better data throughput while sacrificing very little accuracy.
Preferably, the Zhang-Suen refinement algorithm in the step S10 generally refers to an iterative algorithm, and the whole iterative process is divided into two steps: the first step is to circulate all foreground pixel points and mark the pixel points meeting the following conditions as deleted; the second Step is similar to Step One, conditions 1 and 2 are completely consistent, only conditions 3 and 4 are slightly different, a pixel P1 meeting the following conditions is marked to be deleted, the two steps are circulated until no pixel in the two steps is marked to be deleted, and the output result is the skeleton after binary image refinement.
The invention has the following beneficial effects:
the utility model provides a CAD building specialty house plane graph public corridor space center line's accurate identification method, can realize the accurate discernment of public corridor space center line with the help of computer vision technique and traditional image processing algorithm, thereby realize the automatic examination of corridor space, can accurate stable the obtaining house plane graph all public corridor space's center line, thereby for the regular examination of public corridor in the house plane graph provides fine condition, the operation of staff is convenient for, staff's operating efficiency has been improved, is a generalization can be good and the high identification method of rate of accuracy.
Drawings
FIG. 1 is a schematic view of the overall common corridor centerline identification process of the present invention;
FIG. 2 is a schematic diagram of the center line of a public corridor accurately identified on a drawing sheet in a complex form according to the present invention;
FIG. 3 is a schematic diagram of the present invention illustrating the precise identification of the centerline of a Z-shaped common corridor on a drawing sheet;
FIG. 4 is a schematic diagram of the method for accurately identifying the center line of the straight-line-shaped public corridor on the drawing.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
As shown in fig. 1 to 4, in an embodiment of the present invention, a method for accurately identifying a center line of a public corridor space in a building professional home plan includes the following specific operation steps:
s1, analyzing the CAD drawing to obtain basic information such as a primitive and a layer in the drawing;
s2, recommending the graph layer to a recommended graph layer of the component according to the basic information acquired in the step 1;
s3, acquiring related information such as wall columns, door and window layers and the like from the recommended layer in the step 2;
s4, combining the primitive of the layer of the door and window in the step 3 to obtain a door and window component bbox;
s5, according to all the door and window members bbox obtained in the step 4, digging out small drawings of all doors and windows from the base drawings printed by the drawing;
s6, sending the small graph of the door and window member obtained in the step 5 into a MobileNet V1 deep convolution neural network for classification, and obtaining the accurate category of the door and window member after classification;
s7, printing the wall column graphic primitive in the step 3 on a base map, combining the door and window member information in the step 6, performing space segmentation by using image processing, and acquiring the function of each space;
s8, acquiring a public walkway space from all the spaces in the step 7, and then matting out a small picture of the space;
s9, zooming the small graph in the step 8 by utilizing an image processing technology;
s10, transmitting the zoomed small image in the step 9 into a Zhang-Suen thinning algorithm to obtain a central line of a corridor space;
s11, zooming the center line obtained in the step 10 to further obtain the center line of the corridor space in the original image;
s12, extending the central line obtained in the step 11 to further obtain an end point of the corridor space;
and S13, storing the central line obtained in the step 11 and the space end point of the corridor obtained in the step 12.
Wherein, the primitive in the step S1 refers to graphics data, and what corresponds to the primitive is a visible entity on the drawing interface; the image layer refers to a film containing elements such as characters or figures, the film is stacked together in sequence and combined to form the final effect of the page, and the image layer can accurately position the elements on the page; texts, pictures, tables and plug-ins can be added into the layers, and the layers can be nested in the layers.
Wherein a component is an exchangeable part that is actually present in the system, which performs a specific function, conforms to a set of interface standards and can implement a set of interfaces, the component representing a physical implementation of a part of the system, comprising software code or its equivalent, and in the figures the component is represented as a rectangle with a label.
In S6, bbox is a regression frame, a predicted frame value is input at first, and the original classification problem is only input as one graph, but the input graph now includes position information of the input graph in the original graph; the input graph can extract a feature vector through convolutional network learning; one goal of target detection is to expect the last bounding box to coincide with the ground route.
The Mobile Net V1 in the step S6 refers to a network architecture issued by Google, and the main innovation point is to replace the ordinary convolution with a deep separable convolution and use a width multiplier to reduce the number of parameters, which can trade off better data throughput while sacrificing very little precision.
Wherein, Zhang-Suen refinement algorithm in the step S10 generally refers to an iterative algorithm, and the whole iterative process is divided into two steps: the first step is to circulate all foreground pixel points and mark the pixel points meeting the following conditions as deleted; the second Step is similar to Step One, conditions 1 and 2 are completely consistent, only conditions 3 and 4 are slightly different, a pixel P1 meeting the following conditions is marked to be deleted, the two steps are circulated until no pixel in the two steps is marked to be deleted, and the output result is the skeleton after binary image refinement.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the invention, the scope of which is defined in the appended claims and their equivalents.
Claims (6)
1. A method for accurately identifying the central line of a public corridor space in a building professional house plan is characterized by comprising the following steps: the method for accurately identifying the center line of the public corridor space in the building professional residential plan comprises the following specific operation steps:
s1, analyzing the CAD drawing to obtain basic information such as a primitive and a layer in the drawing;
s2, recommending the graph layer to a recommended graph layer of the component according to the basic information acquired in the step 1;
s3, acquiring related information such as wall columns, door and window layers and the like from the recommended layer in the step 2;
s4, combining the primitive of the layer of the door and window in the step 3 to obtain a door and window component bbox;
s5, according to all the door and window members bbox obtained in the step 4, digging out small drawings of all doors and windows from the base drawings printed by the drawing;
s6, sending the small graph of the door and window member obtained in the step 5 into a MobileNet V1 deep convolution neural network for classification, and obtaining the accurate category of the door and window member after classification;
s7, printing the wall column graphic primitive in the step 3 on a base map, combining the door and window member information in the step 6, performing space segmentation by using image processing, and acquiring the function of each space;
s8, acquiring a public walkway space from all the spaces in the step 7, and then matting out a small picture of the space;
s9, zooming the small graph in the step 8 by utilizing an image processing technology;
s10, transmitting the zoomed small image in the step 9 into a Zhang-Suen thinning algorithm to obtain a central line of a corridor space;
s11, zooming the center line obtained in the step 10 to further obtain the center line of the corridor space in the original image;
s12, extending the central line obtained in the step 11 to further obtain an end point of the corridor space;
and S13, storing the central line obtained in the step 11 and the space end point of the corridor obtained in the step 12.
2. A method for accurately identifying the center line of a common corridor space in a building professional home floor plan according to claim 1, wherein the method comprises the following steps: the graphic primitive in the step S1 refers to graphic data, and the corresponding graphic primitive is a visible entity on the drawing interface; the image layer refers to a film containing elements such as characters or figures, the film is stacked together in sequence and combined to form the final effect of the page, and the image layer can accurately position the elements on the page; texts, pictures, tables and plug-ins can be added into the layers, and the layers can be nested in the layers.
3. A method for accurately identifying the center line of a common corridor space in a building professional home floor plan according to claim 1, wherein the method comprises the following steps: a component is an exchangeable part that is actually present in the system, that performs a specific function, that complies with a set of interface standards and that enables a set of interfaces, and that represents a physical implementation of a part of the system, comprising software code or its equivalent, and that is represented in the drawing as a rectangle with labels.
4. A method for accurately identifying the center line of a common corridor space in a building professional home floor plan according to claim 1, wherein the method comprises the following steps: in step S6, bbox is a regression frame, a predicted frame value is input at first, and the original classification problem is only input as a single graph, but the input graph also includes position information of the graph in the original graph; the input graph can extract a feature vector through convolutional network learning; one goal of target detection is to expect the last bounding box to coincide with the ground route.
5. A method for accurately identifying the center line of a common corridor space in a building professional home floor plan according to claim 1, wherein the method comprises the following steps: mobile Net V1 in the step S6 refers to a network architecture issued by Google, and the main innovation points are that the depth separable convolution replaces the ordinary convolution, and the width multiplier is used for reducing the parameter number, so that better data throughput can be obtained while extremely low precision is sacrificed.
6. A method for accurately identifying the center line of a common corridor space in a building professional home floor plan according to claim 1, wherein the method comprises the following steps: the Zhang-Suen refinement algorithm in the step S10 generally refers to an iterative algorithm, and the whole iterative process is divided into two steps: the first step is to circulate all foreground pixel points and mark the pixel points meeting the following conditions as deleted; the second Step is similar to Step One, conditions 1 and 2 are completely consistent, only conditions 3 and 4 are slightly different, a pixel P1 meeting the following conditions is marked to be deleted, the two steps are circulated until no pixel in the two steps is marked to be deleted, and the output result is the skeleton after binary image refinement.
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