CN113971813A - Accurate identification method for parking spaces in construction professional general plane construction drawing and underground garage construction drawing - Google Patents
Accurate identification method for parking spaces in construction professional general plane construction drawing and underground garage construction drawing Download PDFInfo
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Abstract
The invention belongs to the technical field of CAD drawing identification and computer vision, and discloses a method for accurately identifying parking spaces in a construction professional general plane construction drawing and an underground garage construction drawing, which comprises the following steps: s1, analyzing the CAD drawing to obtain basic information such as a primitive and a layer in the drawing; s2, printing the content in the CAD drawing on a picture according to the basic information acquired in the step S1; s3, extracting a small picture of the parking space from the picture obtained in the step S2, and manually marking the small picture; and S4, training a MobileNet V1 deep convolution neural classification network model by using the labeled small graph obtained in the step S3. The method can accurately and stably obtain all the passenger vehicle parking spaces and commercial vehicle parking spaces in the total plane construction drawing and the underground garage construction drawing, thereby providing good conditions for the rule examination of the passenger vehicle parking spaces and the commercial vehicle parking spaces in the total plane construction drawing and the underground garage construction drawing.
Description
Technical Field
The invention belongs to the technical field of CAD drawing identification and computer vision, and particularly relates to an accurate identification method for parking spaces in a construction professional general plane construction drawing and an underground garage construction drawing.
Background
The CAD construction drawing is a drawing made by using design software such as AutoCAD and the like to make the overall layout of engineering projects, the external shape, internal arrangement, structural construction, internal and external decoration, material manufacturing method, equipment, construction and the like of a building, has the characteristics of complete drawing, accurate expression and specific requirements, is the basis for engineering construction, construction drawing budget planning and construction organization design, is an important technical document for technical management, needs to carefully examine the construction drawing before construction to enter the construction stage in order to ensure the smooth construction, can avoid the influence of the drawing on the use stage after construction is finished, and mainly shows the overall layout of the whole building base and specifically expresses the position, orientation and surrounding environment of a newly-built house (original building, traffic road, etc.) Greening, terrain, and the like), a ground red line is used as a thick dotted line in the general diagram, all newly-built houses to be built can not exceed the red line and meet the standards of fire protection, sunshine, and the like, the building density, the volume fraction, the greenery fraction, the building occupation, the parking space, the road arrangement, and the like in the general diagram should meet the design standards and the design points provided by the local planning bureau, and the underground garage diagram in the CAD construction diagram mainly represents the underground layout of the whole base building, and particularly represents the patterns of the basic situations of the parking space, the pillars, the fire doors, and the like.
Because the conventional CAD construction drawing is inspected manually, which is a repetitive work consuming time and labor, so that inspection personnel can easily miss inspection, along with the wide application of artificial intelligence, some manually completed works can be completed by the artificial intelligence, the artificial intelligence can inspect the parking space specification in the CAD construction drawing, and accurate identification of the parking space in the construction drawing needs to be realized by means of computer vision.
In order to solve the problem, the application provides an accurate identification method for parking spaces in a construction professional general plane construction drawing and an underground garage construction drawing.
Disclosure of Invention
Aiming at the problems, the invention provides an accurate identification method for parking spaces in a construction professional general plane construction drawing and an underground garage construction drawing, and the method has the advantages of good generalization performance and high accuracy.
In order to achieve the purpose, the invention provides the following technical scheme: a method for accurately identifying parking spaces in a construction professional general plane construction drawing and an underground garage construction drawing comprises the following steps:
s1, analyzing the CAD drawing to obtain basic information such as a primitive and a layer in the drawing;
s2, printing the content in the CAD drawing on a picture according to the basic information acquired in the step S1;
s3, extracting a small picture of the parking space from the picture obtained in the step S2, and manually marking the small picture;
s4, training a MobileNet V1 deep convolution neural classification network model by using the labeled small graph obtained in the step S3;
s5, screening out the primitives with high probability of forming the parking spaces according to the layer information of the primitives acquired in the step S1;
s6, acquiring short-edge constituting primitives and long-edge constituting primitives which are probably the parking spaces of the passenger cars from all the linear primitives screened out in the step S5 according to the experience sizes of the parking spaces of the passenger cars;
s7, acquiring short-edge constituting primitives and long-edge constituting primitives which are probably the commercial vehicle parking spaces from all the linear primitives screened out in the step S5 according to the experience size of the commercial vehicle parking spaces;
s8, finding a first long-edge straight line primitive which is vertical to the short-edge candidate primitive and is connected with the end point of the short-edge candidate primitive from the long-edge candidate primitive obtained in the step S6 according to the short-edge candidate primitive obtained in the step S6;
s9, then according to the short edge candidate primitive information of the passenger car parking space obtained in the step S6;
s10, finding a first long-edge straight line primitive which is vertical to the short-edge candidate primitive and is connected with an end point of the short-edge candidate primitive from the long-edge candidate primitives of the commercial vehicle parking space obtained in the step S7 according to the short-edge candidate primitives of the commercial vehicle parking space obtained in the step S7;
s11, then according to the short edge candidate primitive information of the parking space of the commercial vehicle obtained in the step S7;
s12, according to the two pieces of long-side straight line primitive information of the passenger car parking space obtained in the steps S8 and S9;
s13, according to the two pieces of long-side straight line primitive information of the commercial vehicle parking space obtained in the steps S10 and S11;
s14, combining the two short-side linear primitives and the two long-side linear primitives of the passenger car parking space obtained in the step S12 into a passenger car parking space, and obtaining an external rectangular frame of the passenger car parking space;
s15, combining the two short-side linear primitives and the two long-side linear primitives of the commercial vehicle parking space obtained in the step S13 into a commercial vehicle parking space, and obtaining an external rectangular frame of the commercial vehicle parking space;
s16, deleting the primitives that the two passenger car parking spaces obtained in the step S12 form short-edge straight lines from the primitives that the short-edge candidate primitives of the passenger car parking spaces are obtained in the step S6, and deleting the primitives that the two passenger car parking spaces obtained in the step S12 form long-edge straight lines from the primitives that the long-edge candidate primitives of the passenger car parking spaces are obtained in the step S6;
s17, deleting the primitives that the two commercial vehicle parking spaces obtained in the step S13 form short-edge straight lines from the primitives that the short-edge candidate primitives of the commercial vehicle parking spaces are obtained in the step S7, and deleting the primitives that the two commercial vehicle parking spaces obtained in the step S13 form long-edge straight lines from the primitives that the long-edge candidate primitives of the commercial vehicle parking spaces are obtained in the step S7;
s18, utilizing the short edge candidate primitives and the long edge candidate primitives of the rest passenger car parking space obtained in the step S16, entering the step S8, and continuing to match the passenger car parking space until the number of the short edge candidate primitives and the long edge candidate primitives of the rest passenger car parking space is zero;
s19, utilizing the short edge candidate primitives and the long edge candidate primitives of the rest commercial vehicle parking space acquired in the step S17, entering the step S8, and continuing to match the commercial vehicle parking space until the number of the short edge candidate primitives and the long edge candidate primitives of the rest commercial vehicle parking space are zero;
s20, digging out a small picture of the parking space of the passenger car from the complete picture printed by the drawing according to the external rectangular frames of the parking spaces of all the passenger cars obtained in the step S18;
s21, according to all the circumscribed rectangular frames of the commercial vehicle parking spaces acquired in the step S19, digging out small pictures of the commercial vehicle parking spaces from the complete pictures printed by the drawings;
and S22, inputting the passenger car parking space minimap obtained in the step S20 and the commercial car parking space minimap obtained in the step S21 into the MobileNet V1 deep convolutional neural network obtained in the step S4, and obtaining the classification result of each minimap.
As a preferred technical solution of the present invention, the primitive information in S9 refers to that a second long-side straight-line primitive which is perpendicular to and connected to the long-side candidate primitives obtained in step 6 and has an end point connected to the long-side candidate primitives obtained in step 8 and a length different from the long-side candidate primitives obtained in step 8 is found, and whether the long-side straight-line primitive is a passenger car parking space can be determined by the displayed primitive information.
As a preferred technical solution of the present invention, the primitive information in S11 refers to that a second long-side straight line primitive which is perpendicular to and connected to the long-side candidate primitives obtained in step 7 and has an end point connected to the long-side candidate primitives obtained in step 10 and a length different from the long-side candidate primitives obtained in step 10 is found, and whether the long-side straight line primitive is a commercial vehicle parking space can be determined through the displayed primitive information.
As a preferred technical solution of the present invention, the primitive information in S12 refers to a second short-side straight line primitive of the parking space of the passenger car searched from the short-side candidate primitives of the parking space of the passenger car obtained in step 6, and the parking space of the passenger car can be easily found out by using this rule, which facilitates the checking work and makes the checking work more convenient.
As a preferred technical solution of the present invention, the primitive information in S13 refers to a second short-side linear primitive of the parking space for the commercial vehicle, which is searched from the short-side candidate primitives obtained in step 7, and the parking space for the commercial vehicle can be easily found out by using this rule, which is convenient for the examination work, so that the examination work is more convenient.
Compared with the prior art, the invention has the following beneficial effects:
1. the method can accurately and stably obtain all the passenger vehicle parking spaces and commercial vehicle parking spaces in the total plane construction drawing and the underground garage construction drawing, thereby providing good conditions for the rule examination of the passenger vehicle parking spaces and the commercial vehicle parking spaces in the total plane construction drawing and the underground garage construction drawing.
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Fig. 1 is a schematic view of a parking space identification process according to the present invention.
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, the invention provides a method for accurately identifying parking spaces in a construction professional general plane construction drawing and an underground garage construction drawing, which comprises the following steps:
s1, analyzing the CAD drawing to obtain basic information such as a primitive and a layer in the drawing;
s2, printing the content in the CAD drawing on a picture according to the basic information acquired in the step S1;
s3, extracting a small picture of the parking space from the picture obtained in the step S2, and manually marking the small picture;
s4, training a MobileNet V1 deep convolution neural classification network model by using the labeled small graph obtained in the step S3;
s5, screening out the primitives with high probability of forming the parking spaces according to the layer information of the primitives acquired in the step S1;
s6, acquiring short-edge constituting primitives and long-edge constituting primitives which are probably the parking spaces of the passenger cars from all the linear primitives screened out in the step S5 according to the experience sizes of the parking spaces of the passenger cars;
s7, acquiring short-edge constituting primitives and long-edge constituting primitives which are probably the commercial vehicle parking spaces from all the linear primitives screened out in the step S5 according to the experience size of the commercial vehicle parking spaces;
s8, finding a first long-edge straight line primitive which is vertical to the short-edge candidate primitive and is connected with the end point of the short-edge candidate primitive from the long-edge candidate primitive obtained in the step S6 according to the short-edge candidate primitive obtained in the step S6;
s9, then according to the short edge candidate primitive information of the passenger car parking space obtained in the step S6;
s10, finding a first long-edge straight line primitive which is vertical to the short-edge candidate primitive and is connected with an end point of the short-edge candidate primitive from the long-edge candidate primitives of the commercial vehicle parking space obtained in the step S7 according to the short-edge candidate primitives of the commercial vehicle parking space obtained in the step S7;
s11, then according to the short edge candidate primitive information of the parking space of the commercial vehicle obtained in the step S7;
s12, according to the two pieces of long-side straight line primitive information of the passenger car parking space obtained in the steps S8 and S9;
s13, according to the two pieces of long-side straight line primitive information of the commercial vehicle parking space obtained in the steps S10 and S11;
s14, combining the two short-side linear primitives and the two long-side linear primitives of the passenger car parking space obtained in the step S12 into a passenger car parking space, and obtaining an external rectangular frame of the passenger car parking space;
s15, combining the two short-side linear primitives and the two long-side linear primitives of the commercial vehicle parking space obtained in the step S13 into a commercial vehicle parking space, and obtaining an external rectangular frame of the commercial vehicle parking space;
s16, deleting the primitives that the two passenger car parking spaces obtained in the step S12 form short-edge straight lines from the primitives that the short-edge candidate primitives of the passenger car parking spaces are obtained in the step S6, and deleting the primitives that the two passenger car parking spaces obtained in the step S12 form long-edge straight lines from the primitives that the long-edge candidate primitives of the passenger car parking spaces are obtained in the step S6;
s17, deleting the primitives that the two commercial vehicle parking spaces obtained in the step S13 form short-edge straight lines from the primitives that the short-edge candidate primitives of the commercial vehicle parking spaces are obtained in the step S7, and deleting the primitives that the two commercial vehicle parking spaces obtained in the step S13 form long-edge straight lines from the primitives that the long-edge candidate primitives of the commercial vehicle parking spaces are obtained in the step S7;
s18, utilizing the short edge candidate primitives and the long edge candidate primitives of the rest passenger car parking space obtained in the step S16, entering the step S8, and continuing to match the passenger car parking space until the number of the short edge candidate primitives and the long edge candidate primitives of the rest passenger car parking space is zero;
s19, utilizing the short edge candidate primitives and the long edge candidate primitives of the rest commercial vehicle parking space acquired in the step S17, entering the step S8, and continuing to match the commercial vehicle parking space until the number of the short edge candidate primitives and the long edge candidate primitives of the rest commercial vehicle parking space are zero;
s20, digging out a small picture of the parking space of the passenger car from the complete picture printed by the drawing according to the external rectangular frames of the parking spaces of all the passenger cars obtained in the step S18;
s21, according to all the circumscribed rectangular frames of the commercial vehicle parking spaces acquired in the step S19, digging out small pictures of the commercial vehicle parking spaces from the complete pictures printed by the drawings;
and S22, inputting the passenger car parking space minimap obtained in the step S20 and the commercial car parking space minimap obtained in the step S21 into the MobileNet V1 deep convolutional neural network obtained in the step S4, and obtaining the classification result of each minimap.
The primitive information in S9 is that a second long-side straight-line primitive which is perpendicular to the long-side candidate primitives obtained in step 6, connected to the end point of the long-side candidate primitives, and has a different long side from the long-side candidate primitives obtained in step 8 and the same length is found, and whether the long-side straight-line primitive is a passenger car parking space can be determined by the displayed primitive information.
The primitive information in S11 is to find a second long-edge straight line primitive which is perpendicular to and connected to the long-edge candidate primitives obtained in step 7 and has a same length as the long edge obtained in step 10, and determine whether the long-edge straight line primitive is a commercial vehicle parking space according to the displayed primitive information.
The primitive information in S12 refers to that the second short-side linear primitive of the parking space of the passenger car is searched from the short-side candidate primitives obtained in step 6, and the parking space of the passenger car can be easily found out by using this rule, which facilitates the checking work and makes the checking work more convenient.
The primitive information in S13 refers to that the second short-side linear primitive of the commercial vehicle parking space is searched from the short-side candidate primitives obtained in step 7, and the commercial vehicle parking space can be easily found out by using this rule, which is convenient for the inspection work, so that the inspection work is more convenient.
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 (5)
1. A method for accurately identifying parking spaces in a construction professional general plane construction drawing and an underground garage construction drawing is characterized by comprising the following steps:
s1, analyzing the CAD drawing to obtain basic information such as a primitive and a layer in the drawing;
s2, printing the content in the CAD drawing on a picture according to the basic information acquired in the step S1;
s3, extracting a small picture of the parking space from the picture obtained in the step S2, and manually marking the small picture;
s4, training a MobileNet V1 deep convolution neural classification network model by using the labeled small graph obtained in the step S3;
s5, screening out the primitives with high probability of forming the parking spaces according to the layer information of the primitives acquired in the step S1;
s6, acquiring short-edge constituting primitives and long-edge constituting primitives which are probably the parking spaces of the passenger cars from all the linear primitives screened out in the step S5 according to the experience sizes of the parking spaces of the passenger cars;
s7, acquiring short-edge constituting primitives and long-edge constituting primitives which are probably the commercial vehicle parking spaces from all the linear primitives screened out in the step S5 according to the experience size of the commercial vehicle parking spaces;
s8, finding a first long-edge straight line primitive which is vertical to the short-edge candidate primitive and is connected with the end point of the short-edge candidate primitive from the long-edge candidate primitive obtained in the step S6 according to the short-edge candidate primitive obtained in the step S6;
s9, then according to the short edge candidate primitive information of the passenger car parking space obtained in the step S6;
s10, finding a first long-edge straight line primitive which is vertical to the short-edge candidate primitive and is connected with an end point of the short-edge candidate primitive from the long-edge candidate primitives of the commercial vehicle parking space obtained in the step S7 according to the short-edge candidate primitives of the commercial vehicle parking space obtained in the step S7;
s11, then according to the short edge candidate primitive information of the parking space of the commercial vehicle obtained in the step S7;
s12, according to the two pieces of long-side straight line primitive information of the passenger car parking space obtained in the steps S8 and S9;
s13, according to the two pieces of long-side straight line primitive information of the commercial vehicle parking space obtained in the steps S10 and S11;
s14, combining the two short-side linear primitives and the two long-side linear primitives of the passenger car parking space obtained in the step S12 into a passenger car parking space, and obtaining an external rectangular frame of the passenger car parking space;
s15, combining the two short-side linear primitives and the two long-side linear primitives of the commercial vehicle parking space obtained in the step S13 into a commercial vehicle parking space, and obtaining an external rectangular frame of the commercial vehicle parking space;
s16, deleting the primitives that the two passenger car parking spaces obtained in the step S12 form short-edge straight lines from the primitives that the short-edge candidate primitives of the passenger car parking spaces are obtained in the step S6, and deleting the primitives that the two passenger car parking spaces obtained in the step S12 form long-edge straight lines from the primitives that the long-edge candidate primitives of the passenger car parking spaces are obtained in the step S6;
s17, deleting the primitives that the two commercial vehicle parking spaces obtained in the step S13 form short-edge straight lines from the primitives that the short-edge candidate primitives of the commercial vehicle parking spaces are obtained in the step S7, and deleting the primitives that the two commercial vehicle parking spaces obtained in the step S13 form long-edge straight lines from the primitives that the long-edge candidate primitives of the commercial vehicle parking spaces are obtained in the step S7;
s18, utilizing the short edge candidate primitives and the long edge candidate primitives of the rest passenger car parking space obtained in the step S16, entering the step S8, and continuing to match the passenger car parking space until the number of the short edge candidate primitives and the long edge candidate primitives of the rest passenger car parking space is zero;
s19, utilizing the short edge candidate primitives and the long edge candidate primitives of the rest commercial vehicle parking space acquired in the step S17, entering the step S8, and continuing to match the commercial vehicle parking space until the number of the short edge candidate primitives and the long edge candidate primitives of the rest commercial vehicle parking space are zero;
s20, digging out a small picture of the parking space of the passenger car from the complete picture printed by the drawing according to the external rectangular frames of the parking spaces of all the passenger cars obtained in the step S18;
s21, according to all the circumscribed rectangular frames of the commercial vehicle parking spaces acquired in the step S19, digging out small pictures of the commercial vehicle parking spaces from the complete pictures printed by the drawings;
and S22, inputting the passenger car parking space minimap obtained in the step S20 and the commercial car parking space minimap obtained in the step S21 into the MobileNet V1 deep convolutional neural network obtained in the step S4, and obtaining the classification result of each minimap.
2. The method for accurately identifying the parking spaces in the construction professional general plane construction drawing and the underground garage construction drawing according to claim 1, is characterized in that: the primitive information in S9 refers to finding a second long-edge straight line primitive which is perpendicular to the long-edge candidate primitives obtained in step 6, connected to the end point of the long-edge candidate primitives, and has a different length from the long-edge candidate primitives obtained in step 8.
3. The method for accurately identifying the parking spaces in the construction professional general plane construction drawing and the underground garage construction drawing according to claim 1, is characterized in that: the primitive information in S11 refers to finding a second long-edge straight line primitive which is perpendicular to and connected to the long-edge candidate primitives obtained in step 7 and has a different length from the long-edge candidate primitives obtained in step 10.
4. The method for accurately identifying the parking spaces in the construction professional general plane construction drawing and the underground garage construction drawing according to claim 1, is characterized in that: the primitive information in S12 refers to the second short-side straight line primitive of the parking space of the passenger car searched from the short-side candidate primitives of the parking space of the passenger car obtained in step 6.
5. The method for accurately identifying the parking spaces in the construction professional general plane construction drawing and the underground garage construction drawing according to claim 1, is characterized in that: the primitive information in S13 refers to the second short-side straight line primitive of the commercial vehicle parking space searched from the short-side candidate primitives of the commercial vehicle parking space obtained in step 7.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116110072A (en) * | 2023-04-12 | 2023-05-12 | 江西少科智能建造科技有限公司 | CAD drawing analysis method and system |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110517223A (en) * | 2019-07-11 | 2019-11-29 | 万翼科技有限公司 | Drawing review method, apparatus, computer equipment and storage medium |
| CN112883799A (en) * | 2021-01-20 | 2021-06-01 | 上海品览智造科技有限公司 | Method for accurately identifying slope in CAD (computer-aided design) slope big sample graph |
-
2021
- 2021-10-25 CN CN202111238995.1A patent/CN113971813B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN110517223A (en) * | 2019-07-11 | 2019-11-29 | 万翼科技有限公司 | Drawing review method, apparatus, computer equipment and storage medium |
| CN112883799A (en) * | 2021-01-20 | 2021-06-01 | 上海品览智造科技有限公司 | Method for accurately identifying slope in CAD (computer-aided design) slope big sample graph |
Non-Patent Citations (1)
| Title |
|---|
| 贾哲明, 付永刚, 戴国忠: "建筑平面图纸理解中关于符号识别的新方法", 计算机工程与应用, no. 05, 1 May 2004 (2004-05-01) * |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116110072A (en) * | 2023-04-12 | 2023-05-12 | 江西少科智能建造科技有限公司 | CAD drawing analysis method and system |
| CN116110072B (en) * | 2023-04-12 | 2023-08-15 | 江西少科智能建造科技有限公司 | CAD drawing analysis method and system |
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