A two-stage-based robot steel material single identification method and system

Abstract

The application discloses a two-stage-based robot steel reinforcement material order identification method and system, and the two-stage-based robot steel reinforcement material order identification method comprises the following steps: step S1, steel reinforcement material order data processing; step S2, steel reinforcement shape parameter acquisition; step S3, steel reinforcement process category acquisition; and step S4, processing process acquisition; wherein the two-stage-based robot steel reinforcement material order identification system comprises the following steps: step S1, a steel reinforcement material order data processing module; step S2, a steel reinforcement shape parameter extraction module; step S3, a steel reinforcement shape parameter extraction module; and step S4, a steel reinforcement processing process acquisition module. The application proposes a convenient steel reinforcement simple diagram shape parameter extraction method which does not need to be labeled with data, and can automatically complete steel reinforcement material order acquisition and identification and automatically acquire a steel reinforcement processing process.

Description

Technical Field

[0001] This invention belongs to the field of robot control, specifically relating to a two-stage robot rebar material list recognition method and system. Background Technology

[0002] With the advent of the artificial intelligence era, more and more tasks are becoming intelligent and automated. The construction industry, as one of the largest industries, is moving towards robots replacing workers in complex and harsh environments. In construction, steel reinforcement cutting is a crucial step in concrete structure construction, involving core tasks such as steel reinforcement type identification, quantity counting, and location positioning. Its efficiency and accuracy directly affect construction progress and project quality. Currently, the rebar loading process mainly relies on manual identification of drawings. This method depends on experience, and architectural construction drawings often contain dense rebar annotations, making manual processing inefficient. Furthermore, existing automated identification technologies face several bottlenecks in this field: firstly, rebar drawings contain numerous small, densely packed targets with significant scale differences and complex semantic relationships, making single-stage detection models prone to missed or false detections; secondly, complex large-scale models are computationally intensive and cannot meet the demands of real-time robotic operations. Additionally, the construction industry offers diverse drawing formats (such as CAD, PDF scans, and image formats), and existing systems exhibit poor compatibility with these formats.

[0003] Therefore, for the steel bar feeding scenario of construction robots, there is an urgent need for a material list recognition method that takes into account recognition accuracy, efficiency and compatibility, so as to solve the pain points of manual recognition and the limitations of existing technologies, and promote the deep implementation of construction automation. Summary of the Invention

[0004] The technical problem to be solved by this invention is that existing automatic material list image recognition technology cannot simultaneously achieve both recognition accuracy and recognition speed in the steel bar feeding process. The invention aims to resolve the contradiction between recognition efficiency and error rate by combining traditional image recognition with a custom deep network.

[0005] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is to propose a two-stage robot steel bar material list recognition method and system.

[0006] The technical solution adopted in this invention is: a two-stage robot rebar list recognition method, comprising the following steps: Step S1: Rebar material list data processing; Export the material list data file according to the type of software used, and statistically analyze and organize all data formats. Process all types of rebar material list data into a unified format, which can be, but is not limited to, Excel format. Based on the unified data format, extract the rebar sketches from the material list data in batches. Step S2: Obtaining the shape parameters of the reinforcing bars; The simplified diagram of the reinforcing bars is preprocessed. Numerical regions are extracted from the diagram using traditional image processing methods. Then, based on feature matching algorithms and predefined digit templates, the shape parameters of the reinforcing bars are identified and located individually. Finally, the digits are combined according to their positional relationships to obtain parameters representing the shape of the reinforcing bars. Step S3: Obtain the rebar process category; Considering that reinforcement drawings are generally small in size, and often non-square with diverse dimensions, directly processing them using existing depth networks presents size issues. Therefore, an improvement to the transformer-based vit framework is made to adapt to small targets and non-square inputs. The improved vit framework is then used to classify the reinforcement drawings, resulting in corresponding process categories. Step S4: Obtain the processing steps; Based on a predefined database of relationships between simplified diagram types and parameters, and other information from the material list obtained through the software, the processing steps for this type of steel reinforcement are determined. The numerical recognition method shows that the numbers on both sides of the steel bar diagram are 150. The diagram type recognition method shows that the steel bar type is 3 (from the fourth category in the predefined categories), therefore there are two protruding steel bar ends and the length is 11.9. In addition, the diameter of the steel bar is C8 from the file line index after the unified data format (obtained in step S1). Combining this information, the processing steps are obtained, including the total amount of steel bar used L = (150)*2+(150)*2+(0*7.8*8)+(11.9*2*8) and the number of steel bars.

[0007] Preferably, the preprocessing in step S2 includes binarizing the simplified steel reinforcement diagram, scaling and filtering the binarized image for noise reduction, and using traditional image processing methods to extract digital regions from the simplified diagram, including but not limited to first extracting all contours from the binarized image and then filtering the digital contours based on features such as the area of ​​the contours. Preferably, the predefined digital template in step S2 can be a set of digital regions pre-selected from a large amount of data, and the feature matching algorithm can be based on matching the shape features of the numbers in the grayscale image, or any image processing algorithm that can perform feature matching between the digital regions extracted from the simplified image and the predefined digital template. The template-based feature similarity matching algorithm can be expressed as: ; in, For predefined digital template diagrams, The image shows the digital region to be identified, and )represent and The feature similarity between them, where index is the index of the maximum similarity. The numbers to be identified.

[0008] Specifically, after digital identification and positioning, the combined numbers can be filtered based on prior knowledge of the shape parameters of the reinforcing bars and the positional relationships between the numbers.

[0009] Preferably, the improved transformer-based vit framework described in step S3 includes: firstly, analyzing the training data, including the dimensions of the simplified diagrams and their aspect ratios. After analysis, representative values ​​such as the median of the simplified diagram dimensions can be selected as the input dimensions of the vit network. Simultaneously, to accommodate the non-square nature of the reinforcement diagrams, non-square convolution kernels are used for patch operations in vit. The improved transformer-based vit model can employ data augmentation during the training phase to compensate for insufficient software data collection. Considering that the process type of the reinforcement diagram is independent of the numerical values ​​on the diagram and only related to the line layout, a series of parameter values ​​can be randomly generated, and the original parameters can be replaced using the parameter positions obtained in step S2, thereby enriching the data.

[0010] A two-stage robotic rebar billet recognition system includes the following steps: Step S1: Rebar Material List Data Processing Module; The software retrieves the rebar material list file, processes it into a predefined format, extracts the rebar sketches from all rows of the material list file, converts them into a unified format file, and includes all the rebar sketches as the output of this module. Step S2: Rebar shape parameter extraction module; This module batch-reads simplified rebar diagrams, extracts all contours from the diagrams using a contour extraction algorithm, filters out numerical contours based on the area features of the contours, extracts numbers from the rebar diagrams using a template-based feature matching algorithm, and obtains the shape parameters of the rebars based on the positional relationships between the numbers, which are then used as the output of this module. Step S3: Rebar shape parameter extraction module; Batch read simplified rebar diagrams and preprocess them to the dimensions input to the network, including but not limited to scaling and cropping. Input the simplified rebar diagrams into a pre-trained type classification network, and the output of this module is the types of all simplified rebar diagrams. Step S4: Rebar processing procedure acquisition module; The system reads other parameters of each line of steel bars from the output file of the steel bar material list data processing module, the steel bar process type output by the steel bar process category extraction module, and the steel bar shape parameters output by the steel bar shape parameter extraction module, and calculates the processing procedures based on certain rules.

[0011] In summary, due to the adoption of the above technical solution, the beneficial effects of the present invention are: 1. This invention proposes a convenient method for extracting shape parameters from simplified steel bar diagrams that does not require labeled data.

[0012] 2. The two-stage robotic steel bar material list recognition system proposed in this invention can automatically complete the acquisition and recognition of steel bar material lists, as well as the automatic acquisition of steel bar processing procedures.

[0013] 3. In view of the small size and diverse dimensions of the steel reinforcement diagrams in the steel reinforcement bill of materials, this invention proposes an improved vit frame based on transformer.

[0014] 4. This invention proposes a two-stage steel bar material list identification method, which can resolve the contradiction between efficiency and error rate. Attached Figure Description

[0015] To facilitate understanding by those skilled in the art, the present invention will be further described below with reference to the accompanying drawings.

[0016] Figure 1 This is a block diagram of a two-stage robotic steel rebar material single-image recognition system.

[0017] Figure 2 This is a digital contour map extracted based on traditional image processing methods.

[0018] Figure 3 This is a graph showing the parameter results identified based on traditional image processing methods.

[0019] Figure 4 A network diagram for an improved transformer-based vit framework.

[0020] Figure 5 This is a simplified diagram of the reinforcing steel bars. Detailed Implementation

[0021] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0022] Example 1: The following describes a two-stage robot rebar billet identification method provided by the present invention, including the following steps: Step S1: Rebar Material List Data Processing We will compile and organize all data formats, including but not limited to PDF and Excel, based on the types of rebar detailing software available on the market (such as E-rebar quantity calculation software). We will process all types of rebar material list data into a unified format, which can be, but is not limited to, Excel. Based on this unified data format, we will batch extract simplified rebar diagrams from the material list files.

[0023] Step S2: Obtaining Rebar Shape Parameters The simplified diagram of the reinforcing bars is preprocessed. Digital regions on the diagram are extracted based on traditional image processing methods. The shape parameters of the reinforcing bars are digitally identified and located based on feature matching algorithms and predefined digital templates. The parameters representing the shape of the reinforcing bars are obtained by combining the numbers according to their positional relationships.

[0024] Specifically, preprocessing includes selecting an appropriate threshold to binarize the simplified rebar diagram, scaling the binarized image, and filtering for noise reduction. The specific scaling value depends on the size of the template digits. The preprocessing steps can be represented as follows:

[0025] Specifically, traditional image processing methods for extracting numerical regions from a simplified diagram include, but are not limited to, first using OpenCV functions to extract all contours from the binarized image, and then filtering the numerical contours based on features such as contour area. The simplified diagram contains numbers representing rebar parameters and lines indicating the direction of the construction process. The area of ​​the rectangles corresponding to the line contours is significantly larger than the rectangles corresponding to other numerical contours, and the numbers are of consistent font size, close in position, and occupy roughly the same proportion of the image. Therefore, we can first remove large-area contours based on their rectangle area, then remove contours that are far from all other numerical positions, and finally remove contours whose rectangle area deviates from the average among those that are close in position.

[0026] Specifically, a predefined digital template can be a set of digital regions pre-selected from a large amount of data.

[0027] Specifically, feature matching algorithms can employ matching based on the shape features of numbers in grayscale images, as well as any image processing algorithm capable of matching the extracted digit regions from a simplified image with predefined digit templates. A template-based feature similarity matching algorithm can be expressed as: ; in, For predefined digital template diagrams, The image shows the digital region to be identified. )represent and The feature similarity between them, where index is the index of the maximum similarity. The numbers to be identified.

[0028] Specifically, after digital identification and positioning, the combined numbers can be filtered based on prior knowledge of the shape parameters of the reinforcing bars and the positional relationships between the numbers.

[0029] Step S3: Obtaining the Reinforcing Steel Process Category Considering that the dimensions of reinforcement drawings are generally small, and they are often non-square and vary in size, directly processing these drawings using existing depthwise networks presents size issues. Therefore, an improvement to the transformer-based vit framework is implemented by modifying the network's input dimensions and the convolution kernels in the patch blocks to adapt the network to small targets and non-square inputs. The improved vit framework is then used to classify the reinforcement drawings and obtain the corresponding process categories.

[0030] Specifically, the improved Transformer-based vit framework includes: first, analyzing the training data, including the dimensions and aspect ratios of the simplified diagrams. After analysis, representative values ​​such as the median of the simplified diagram dimensions can be selected as the input dimensions for the vit network. Simultaneously, to accommodate the non-square nature of the reinforcement diagrams, the patch operation in vit uses a 3x5 non-square convolution kernel. The overall network frame is as follows: Figure 4 As shown.

[0031] Specifically, the improved transformer-based vit model can employ data augmentation during the training phase to compensate for the inadequacy of software data collection. Considering that the process type of the reinforcement diagram is independent of the numerical values ​​on the diagram and only related to the line layout, a series of parameter values ​​can be randomly generated and the original parameters can be replaced using the parameter positions obtained in step S2, thereby enriching the data.

[0032] Step S4: Obtaining processing steps Based on a predefined database of relationships between simplified diagram types and parameters, and other information from the material list obtained through the software, the processing steps for this type of steel reinforcement are determined. For example... Figure 5As shown, the numbers on both sides of the steel bar diagram are 150, obtained through digital recognition. The steel bar type is identified as 3 (from the fourth category in the predefined categories), indicating two protruding steel bar ends with a length of 11.9. Furthermore, the diameter of the steel bar is C8, obtained from the file line index after unifying the data format. Combining this information, the processing steps are determined, including the total amount of steel bar used (L = (150)*2 + (150)*2 + (0*7.8*8) + (11.9*2*8) and the number of steel bars.

[0033] A two-stage robotic rebar billet recognition system includes the following steps: Step S1: Rebar Material List Data Processing Module The software retrieves the rebar material list file, processes the file into a predefined format, extracts the rebar sketches from all rows of the material list file, converts them into a unified format file, and uses all the rebar sketches as the output of this module.

[0034] Step S2: Rebar Shape Parameter Extraction Module The module reads batch reinforcement drawings, obtains all contours on the drawings based on contour extraction algorithms, filters out digital contours based on the area features of the contours, extracts numbers on the reinforcement drawings based on template feature matching algorithms, and obtains the shape parameters of the reinforcement based on the positional relationship between the numbers, which are then used as the output of this module.

[0035] Step S3: Rebar Process Category Extraction Module Batch read rebar sketches and preprocess them to the dimensions of the network input, including but not limited to scaling and cropping. Input the rebar sketches into a pre-trained type classification network to obtain the types of all rebar sketches as the output of this module.

[0036] Step S4: Rebar Processing Procedure Acquisition Module The system reads other parameters of each line of steel bars from the output file of the steel bar material list data processing module, the steel bar process type output by the steel bar process category extraction module, and the steel bar shape parameters output by the steel bar shape parameter extraction module, and calculates the processing procedures based on certain rules.

[0037] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A two-stage robot-based steel rebar billet recognition method, characterized in that, Includes the following steps: S1. Rebar Material List Data Processing: Export rebar material list data files according to the type of software used, statistically analyze and organize the data format, process different types of rebar material list data into a unified format, and extract rebar sketches from the material list data in batches according to the unified format. S2. Obtaining Rebar Shape Parameters: The simplified rebar diagram is binarized, and the binarized image is scaled and filtered for noise reduction according to the size of the digital template; all contours on the binarized image are extracted, and digital contours are filtered based on the area of ​​the contour rectangle, the position of the contour, and the deviation of the area of ​​the contour rectangle from the mean; a predefined digital template composed of pre-selected digital regions from the data is used to perform feature matching on the filtered digital contours based on the shape features of the digital images in the grayscale image, and individual digital figures are identified and located; the digital figures are combined according to the positional relationship between them, and the combined digital figures are filtered based on prior knowledge of the rebar shape parameters and the positional relationship between them to obtain the rebar shape parameters and their positions in the simplified rebar diagram; S3. Obtaining Reinforcing Steel Process Categories: Analyze the dimensions and aspect ratios of the simplified reinforcing steel diagrams in the training data, select representative values ​​of the simplified steel diagram dimensions as the input dimensions of the Transformer-based ViT network, and perform patch segmentation of the ViT network using a 3×5 non-square convolution kernel; use the ViT network improved by the input dimensions and the 3×5 non-square convolution kernel to classify the simplified steel diagrams and obtain the reinforcing steel process categories; during the training phase of the ViT network, based on the fact that the reinforcing steel process categories are not related to the numerical values ​​in the simplified steel diagrams but are related to the line layout of the simplified steel diagrams, randomly generate parameter values, and replace the original parameters in the simplified steel diagrams with the randomly generated parameter values ​​according to the parameter positions obtained in step S2, in order to expand the training data; S4. Processing procedure acquisition: Based on the predefined diagram type and diagram parameter relationship library, calculate the amount and number of steel bars according to the steel bar shape parameters obtained in step S2, the steel bar process category obtained in step S3, and the steel bar diameter information obtained from the corresponding bill of materials line in the unified data format file, and obtain the corresponding steel bar processing procedure.

2. A two-stage robotic rebar billet recognition system, characterized in that, It includes a rebar material list data processing module, a rebar shape parameter extraction module, a rebar process category extraction module, and a rebar processing process acquisition module; The steel bar material list data processing module is used to: export steel bar material list data files according to the type of software used, statistically analyze and organize the data format, process different types of steel bar material list data into a unified format, and batch extract steel bar sketches from the material list data; wherein, the extraction of steel bar sketches from all rows of the material list file, the conversion of the file into a unified data format and all steel bar sketches are the outputs of this module; The rebar shape parameter extraction module is used to: perform binarization, size scaling, and filtering denoising on the simplified rebar diagram; extract all contours; filter digital contours based on the area of ​​the contour rectangle, the contour position, and the deviation of the contour rectangle area from the mean; use predefined digital templates and perform feature matching based on the digital shape features in the grayscale image to identify and locate individual digits; combine digits according to the positional relationship between digits, and filter them based on prior knowledge of the rebar shape parameters and the positional relationship between digits to obtain the rebar shape parameters and parameter positions; The rebar process category extraction module is used to: analyze the size and aspect ratio of the rebar sketches in the training data, select representative input sizes, perform patch segmentation of the Transformer-based ViT network using a 3×5 non-square convolution kernel, and classify the rebar sketches using the improved ViT network; randomly generate parameter values ​​during the training phase, and replace the original parameters in the rebar sketches with the parameter positions obtained by the rebar shape parameter extraction module to expand the training data; The rebar processing procedure acquisition module is used to: read the rebar diameter information of the corresponding material list line in the unified data format file output by the rebar material list data processing module, the rebar process category output by the rebar process category extraction module, and the rebar shape parameters output by the rebar shape parameter extraction module; calculate the amount and number of rebars based on the predefined simplified diagram type and the relationship library between simplified diagram parameters; and acquire the processing procedure.

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