CN112949463B - Method and system for establishing and detecting aggregate grading rapid detection model - Google Patents
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Abstract
The invention belongs to the technical field of three-dimensional point cloud processing and machine learning, and discloses a method and a system for establishing and detecting an aggregate grading rapid detection model. The method collects three-dimensional point cloud data of coarse aggregates, extracts 52 characteristic factors with strong gear correlation of the three-dimensional aggregates, performs quality regression and category prediction after image pretreatment, and obtains the grading of the aggregates. The method avoids errors caused by subjective factors in the traditional method, has high speed, high efficiency and good robustness, can realize the automation of aggregate grading detection, and provides quality guarantee for road construction.
Description
Technical Field
The invention belongs to the technical field of three-dimensional point cloud processing and machine learning, and particularly relates to a method and a system for establishing and detecting an aggregate grading rapid detection model.
Background
The proportion of aggregate used in pavement construction in concrete is about 70-80%, and the aggregate is an important constituent material of concrete, and the aggregate accounts for the largest cost in asphalt pavement materials. Meanwhile, aggregate grading can also directly influence the service performance of asphalt pavement and the quality of concrete structure engineering.
The general industrial grading detection method for mixed aggregates is to arrange the calibers of the screens layer by layer from large to small through a mixing building with a plurality of layers of screens, and then manually and roughly measure the gear and quality of coarse aggregates. In addition, most of aggregate delivery adopts intermittent grading, namely, one or a plurality of continuous grain grades are removed from the middle in the whole interval of mineral aggregate grain distribution, so that discontinuous grading is formed to improve the production efficiency, thereby causing the loss of grain grade content, and meanwhile, the grading index of the delivered aggregate has great errors due to subjective factors such as screen loopholes, screen inclination and the like, so that the engineering quality of a concrete pavement is seriously influenced.
At present, in the field of machine learning, research work at home and abroad is mainly focused on morphological characteristic evaluation indexes of aggregates, a systematic aggregate shape classification system cannot be formed, and the existing technology only extracts characteristics such as the upper surface and the contour of the aggregates aiming at traditional two-dimensional images, has low correlation of the characteristics on grading measurement parameters such as quality and gears, and is difficult to predict grading and realize automatic detection through an image processing or machine learning method. In addition, the traditional method utilizes single characteristic parameters, and three-dimensional characteristics affecting aggregate grading and shape are not fully considered, so that the grading accuracy is low.
Disclosure of Invention
The invention aims to provide a method and a system for establishing and detecting an aggregate grading rapid detection model, which are used for solving the problems of low aggregate grading accuracy and efficiency in the prior art.
In order to realize the tasks, the invention adopts the following technical scheme:
a method for establishing an aggregate grading rapid detection model comprises the following steps:
step1: acquiring three-dimensional point cloud data of multiple groups of aggregates and three-dimensional point cloud data of a background, and respectively carrying out channel separation on the three-dimensional point cloud data of each group of aggregates and the three-dimensional point cloud data of the background to acquire an original depth map of each group of aggregates in x, y and z channels and a depth map of the background in x, y and z channels;
each group of aggregate comprises at least one aggregate, and gears and quality of all aggregates are collected;
step2: subtracting the original depth map of each group of aggregate in the z channel from the depth map of the background in the z channel respectively to obtain a plurality of z channel depth maps, denoising the plurality of z channel depth maps to obtain a plurality of denoised z channel depth maps;
step3: calculating the area of the connected domain in each denoised z-channel depth map in the step2, and merging the connected domains with the area of the connected domain larger than a first threshold value in each denoised z-channel depth map into a template image to obtain a plurality of template images;
step 4: dividing the template images obtained in the step3 to obtain a plurality of groups of aggregate effective areas, wherein each group of aggregate effective areas comprises I Xr 、I Yr And I Zr Wherein I Zr Representing an effective area obtained by dividing the corresponding z-channel depth map in the step2 by each template image, and I Xr Representing an effective area obtained by segmenting the original depth map of the corresponding aggregate in the step1 in the x channel by each template image, I Yr Representing an effective area obtained by dividing the original depth image of the corresponding aggregate in the step1 in the y channel by each template image;
step 5: converting the multiple groups of aggregate effective areas obtained in the step 4 into aggregate point cloud models according to PCL operators, wherein the aggregate point cloud models comprise multiple aggregate point clouds, and extracting three-dimensional features and two-dimensional features of each aggregate point cloud;
step 6: and (3) taking all three-dimensional features and two-dimensional features of the aggregate point cloud model obtained in the step (5) as feature data sets, establishing an XGBoost model, taking the feature data sets obtained in the step (4) as input, taking the gear and quality of all aggregates obtained in the step (1) as a label set, training the XGBoost model, and taking the trained XGBoost model as an aggregate grading rapid detection model.
Further, the denoising in step2 includes the steps of:
1) Threshold segmentation is carried out on the z-channel depth map;
2) And converting the image subjected to threshold segmentation into a real height image according to the z-axis resolution, denoising the real height image by sequentially executing opening operation and closing operation, and obtaining a denoised z-channel depth image.
Further, in step3, the first threshold is 300 pixels.
Further, each aggregate point cloud in the aggregate point cloud model of step 5 satisfies:
1) The pixel distance between the eight neighborhood point sets is smaller than a second threshold, and the second threshold is 15 pixels;
2) The number of the connected point sets in each aggregate point cloud is larger than a third threshold, and the third threshold is 500 pixels.
A rapid detection method for aggregate grading comprises the following steps:
step I: collecting three-dimensional point cloud data of aggregate to be detected;
step II: acquiring a characteristic dataset of three-dimensional point cloud data of aggregate to be detected according to the steps 1-5 of the method for establishing any aggregate grading rapid detection model;
step III: inputting the characteristic data set of the three-dimensional point cloud data of the aggregate to be detected into the aggregate grading rapid detection model obtained by any one of the aggregate grading rapid detection model building methods, and obtaining the gear and quality of the aggregate to be detected.
The aggregate grading rapid detection system comprises a data acquisition module, a denoising module, a template acquisition module, an aggregate effective area generation module, a feature acquisition module, a model training module and a detection module;
the data acquisition module is used for acquiring three-dimensional point cloud data of a plurality of groups of aggregates and three-dimensional point cloud data of a background, and respectively carrying out channel separation on the three-dimensional point cloud data of each group of aggregates and the three-dimensional point cloud data of the background to acquire an original depth map of each group of aggregates in x, y and z channels and a depth map of the background in x, y and z channels; each group of aggregate comprises at least one aggregate, and gears and quality of all aggregates are collected;
the denoising module is used for subtracting the original depth map of each group of aggregate in the z channel, which is obtained by the data acquisition module, from the depth map of the background in the z channel respectively to obtain a plurality of z channel depth maps, denoising the plurality of z channel depth maps, and obtaining a plurality of denoised z channel depth maps;
the template acquisition module is used for calculating the area of the connected domain in each denoised z-channel depth map in the denoising module, and combining the connected domains with the area of the connected domain larger than a first threshold value in each denoised z-channel depth map into a template image to obtain a plurality of template images;
the aggregate effective area generating module is used for obtaining a plurality of template images by adopting the template obtaining module to divide so as to obtain a plurality of groups of aggregate effective areas, and each group of aggregate effective areas comprises I Xr 、I Yr And I Zr Wherein I Zr Representing an effective area obtained by dividing the corresponding z-channel depth map in the step2 by each template image, and I Xr Representing an effective area obtained by segmenting the original depth map of the corresponding aggregate in the step1 in the x channel by each template image, I Yr Representing an effective area obtained by dividing the original depth image of the corresponding aggregate in the step1 in the y channel by each template image;
the characteristic acquisition module is used for converting a plurality of groups of aggregate effective areas obtained by the aggregate effective area generation module into an aggregate point cloud model according to the PCL operator, wherein the aggregate point cloud model comprises a plurality of aggregate point clouds, and three-dimensional characteristics and two-dimensional characteristics of each aggregate point cloud are extracted;
the model training module is used for taking all three-dimensional features and two-dimensional features of the aggregate point cloud model obtained by the feature obtaining module as feature data sets, establishing an XGBoost model, taking the feature data sets obtained by the aggregate effective area generating module as input, taking the gear and quality of all aggregates as a label set, training the XGBoost model, and taking the trained XGBoost model as an aggregate grading rapid detection model;
the detection module is used for acquiring three-dimensional point cloud data of the aggregate to be detected, acquiring a characteristic data set of the three-dimensional point cloud data of the aggregate to be detected, inputting the characteristic data set of the three-dimensional point cloud data of the aggregate to be detected into the aggregate grading rapid detection model, and acquiring the gear and quality of the aggregate to be detected.
Further, the denoising in the denoising module includes the following steps:
1) Threshold segmentation is carried out on the z-channel depth map;
2) And converting the image subjected to threshold segmentation into a real height image according to the z-axis resolution, denoising the real height image by sequentially executing opening operation and closing operation, and obtaining a denoised z-channel depth image.
Further, the first threshold is 300 pixels.
Further, each aggregate point cloud in the aggregate point cloud model in the belonging feature acquisition module satisfies:
1) The pixel distance between the eight neighborhood point sets is smaller than a second threshold, and the second threshold is 15 pixels;
2) The number of the connected point sets in each aggregate point cloud is larger than a third threshold, and the third threshold is 500 pixels.
Compared with the prior art, the invention has the following technical characteristics:
(1) According to the invention, three-dimensional reconstruction is performed by collecting three-dimensional information of the aggregate, the extracted characteristics have stronger correlation with the characteristics of the aggregate, and real-time and accurate grading detection can be realized.
(2) Traditional grading detection methods rely on manual measurement, which is time-consuming and labor-consuming. The invention researches the aggregate automatic three-dimensional detection method, is quick and efficient, greatly shortens the grading prediction time and provides technical support for guaranteeing the construction quality.
Drawings
FIG. 1 is a pre-processed aggregate depth image of the present invention;
FIG. 2 is an aggregate point cloud visualization generated by the present invention;
FIG. 3 is a comparison of the grading prediction and the true grading achieved by the method of the present invention;
FIG. 4 is an aggregate three-dimensional feature dataset made in accordance with the present invention;
fig. 5 is the result of the gradation calculation of the present invention.
Detailed Description
First, technical words appearing in the present invention are explained:
XGBoost model: the model is based on an artificial intelligent neural network of a massive parallel boosted tree, and is jointly decided by a plurality of associated decision trees.
Opening and closing operations: the open operation means corrosion first and then expansion, and the closed operation means corrosion first and then expansion; the corrosion is represented by taking the center point of the B structure as the center, finding points which can meet the B structure in the A, and the expansion is represented by placing each point of the A structure to the center point of the B structure and expanding the B structure.
Aggregate: one of the main constituent materials of concrete. Including natural aggregate such as crushed stone, pebble, pumice, natural sand, etc., and artificial aggregate such as cinder, slag, haydite, expanded perlite, etc.
Gear of aggregate: the maximum aggregate size classification condition specified in JTG E42-2005 Highway engineering aggregate test procedure is characterized by the unit of the particle size being mm.
The mass of the aggregate: in the present invention, the mass of the individual aggregate particles is in kg.
The embodiment discloses a method for establishing an aggregate grading rapid detection model, which comprises the following steps:
step1: acquiring three-dimensional point cloud data of multiple groups of aggregates and three-dimensional point cloud data of a background, and respectively carrying out channel separation on the three-dimensional point cloud data of each group of aggregates and the three-dimensional point cloud data of the background to acquire an original depth map of each group of aggregates in x, y and z channels and a depth map of the background in x, y and z channels;
each group of aggregate comprises at least one aggregate, and gears and quality of all aggregates are collected;
step2: subtracting the original depth map of each group of aggregate in the z channel from the depth map of the background in the z channel to obtain a plurality of z channel depth maps I Z Denoising the plurality of z-channel depth maps to obtain a plurality of denoised z-channel depth maps;
step3: calculating the area of a connected domain in each denoised z-channel depth map, screening out connected domains with the area of the connected domain larger than a first threshold value in each denoised z-channel depth map, merging the connected domains larger than the first threshold value into a template image, and obtaining a plurality of template images;
step 4: dividing the template images obtained in the step3 to obtain a plurality of groups of aggregate effective areas, wherein each group of aggregate effective areas comprises I Xr 、I Yr And I Zr Wherein I Zr Representing an effective area obtained by dividing the corresponding z-channel depth map in the step2 by each template image, and I Xr Representing an effective area obtained by segmenting the original depth map of the corresponding aggregate in the step1 in the x channel by each template image, I Yr Representing an effective area obtained by dividing the original depth image of the corresponding aggregate in the step1 in the y channel by each template image;
step 5: converting a plurality of groups of aggregate effective areas into an aggregate point cloud model according to a PCL operator, wherein the aggregate point cloud model comprises a plurality of aggregate point clouds, and extracting three-dimensional characteristics and two-dimensional characteristics of each aggregate point cloud;
step 6: and (3) taking all three-dimensional features and two-dimensional features of the aggregate point cloud model obtained in the step (5) as feature data sets, establishing an XGBoost model, taking the feature data sets obtained in the step (4) as input, taking the gear and quality of all aggregates in the step (1) as a label set, training the XGBoost model, and taking the trained XGBoost model as an aggregate grading rapid detection model.
Specifically, the three-dimensional point cloud data in step1 includes a depth map, a millimeter value altitude map, a gray map, an effective data area, a frame count, a time stamp, an encoder position, an encoder sequence, an X-direction offset, an X-direction resolution, a Y-direction resolution, a Z-direction offset, a Z-direction resolution, an image width, an image height, and whether there is a gray value. The height map, the millimeter value height map, the gray level map and the effective data area are stored in a two-dimensional image.
Specifically, in step1, aggregate and background images are separated by channels to obtain I Xi 、I Yi 、I Zi (i=o, b, aggregate and background respectively), where I Xi Record X-axis position information, I Yi Record Y-axis position information, I Zi Height information is recorded.
Specifically, the denoising in step2 includes the steps of:
1) Depth map for z channel I Z Threshold segmentation is carried out, and irrelevant noise points except a background and a target are removed;
2) The image after threshold segmentation is converted into a real height image according to the Z-axis resolution, the real height image is subjected to denoising by sequentially executing opening operation and closing operation, and a denoised Z-channel depth image is obtained, wherein the resolution range of the image generated by the embodiment is 624 x 350-3120 x 1750.
Specifically, the open operation is used to eliminate small interfering objects, where assuming that X and Z are two sets in two-dimensional european space, representing a target aggregate image and one structural element (window), respectively, the open operation is defined as follows:
wherein,,the representation is that the X theta Z corrosion image is restored to the original image after being expanded by the structural element Z; in order to keep the three-dimensional characteristic scale of the aggregate unchanged, the corrosion effect on the edge of the aggregate is reduced by closing operation with the same scale.
Preferably, the two steps of opening operation and closing operation are combined into a group, and 7 times of opening and closing combinations with different scales are executed to obtain a denoised z-channel depth map I Zd The sizes of the structural elements selected each time are respectively 0.5, 1.0, 1.5, 2.0, 2.5, 3.0 and 3.5.
Specifically, in step3, the first threshold is 300 pixels.
Specifically, the segmentation in the step 4 means that the template image is used as a mask to select a corresponding region in the corresponding image to complete segmentation.
Specifically, in step 5, three-channel xyz data are converted into PLY point cloud format through PCL operator so as to convert multiple groups of aggregate effective areas into aggregate point cloud model.
Specifically, each aggregate point cloud in the aggregate point cloud model in step 5 satisfies:
1) The pixel distance between the eight neighborhood point sets is smaller than a second threshold, and the second threshold is 15 pixels;
2) The number of the connected point sets in each aggregate point cloud is larger than a third threshold, and the third threshold is 500 pixels.
Specifically, in step 5, the acquiring the three-dimensional characteristic of each aggregate point cloud includes the following sub-steps:
step1: obtaining a convex hull of the current aggregate point cloud, and obtaining convex hull characteristics;
step2: extracting convex hull center, volume, area, optimal external cube and height parameters by using ConvexHull < pcl > operator;
step3: extracting three-dimensional features of aggregate, e.g. longest axis L of best circumscribing cube 1 Second long axis L 2 And a third long axis L 3 The length values, the equivalent ellipse length and minor axis, etc., are specifically shown in table 1.
TABLE 1
The coordinate system in Step3 is oriented such that the longest edge of the frame is aligned with the x-axis, the second longest edge is aligned with the y-axis, and the smallest edge is aligned with the z-axis.
Specifically, in step 5, the acquiring the two-dimensional characteristic of each aggregate point cloud includes the following sub-steps:
step1: mapping the three-dimensional aggregate point cloud model to a two-dimensional matrix;
step2: the aggregate characteristics of the two-dimensional matrix are extracted by operators such as cvContourArea, cvArcLength of opencv, and the two-dimensional characteristics such as contour length, surface area and the like are obtained, and are specifically shown in table 2.
TABLE 2
Specifically, in step 6, the feature data set is stored in csv format, and each row is represented as an aggregate, wherein, the 1 st row of the parameter header is the aggregate serial number, and the 2 nd to 53 th rows are the extracted 52 three-dimensional feature parameters.
Specifically, in the training process of step 6, the data set is processed according to the following steps: 2: the scale of 2 is divided into a training set, a test set and a verification set, and the XGBoost regression and classification model based on machine learning is input. After a regression tree is established, the three-dimensional features obtained in the step one are compressed to one-dimensional feature vectors, the one-dimensional feature vectors are input into a detection model of the invention for processing, a predicted value of a number group is close to a true value and has generalization capability through greedy strategy and secondary optimization, an output value of a cross entropy loss function is optimized through a gradient descent method, the weight of a network is updated through counter propagation, parameters of each operation are continuously adjusted, the model and the super parameters are continuously improved until loss reaches a minimum value, and then a predicted result of aggregate gear and quality is obtained.
The embodiment also discloses a rapid detection method for aggregate grading, which comprises the following steps:
step I: collecting three-dimensional point cloud data of aggregate to be detected;
step II: acquiring a characteristic dataset of three-dimensional point cloud data of aggregate to be detected according to the steps 1-5 of the method for establishing the aggregate grading rapid detection model;
step III: and inputting the characteristic data set of the three-dimensional point cloud data of the aggregate to be detected into an aggregate grading rapid detection model to obtain the gear and quality of the aggregate to be detected.
The embodiment also discloses a rapid aggregate grading detection system, which comprises a data acquisition module, a denoising module, a template acquisition module, an aggregate effective area generation module, a characteristic acquisition module, a model training module and a detection module;
the data acquisition module is used for acquiring three-dimensional point cloud data of a plurality of groups of aggregates and three-dimensional point cloud data of a background, and respectively carrying out channel separation on the three-dimensional point cloud data of each group of aggregates and the three-dimensional point cloud data of the background to acquire an original depth map of each group of aggregates in x, y and z channels and a depth map of the background in x, y and z channels; each group of aggregate comprises at least one aggregate, and gears and quality of all aggregates are collected;
the denoising module is used for subtracting the original depth map of each group of aggregate in the z channel, which is obtained by the data acquisition module, from the depth map of the background in the z channel respectively to obtain a plurality of z channel depth maps, denoising the plurality of z channel depth maps, and obtaining a plurality of denoised z channel depth maps;
the template acquisition module is used for calculating the area of the connected domain in each denoised z-channel depth map in the denoising module, and combining the connected domains with the area of the connected domain larger than a first threshold value in each denoised z-channel depth map into a template image to obtain a plurality of template images;
the aggregate effective area generating module is used for obtaining a plurality of template images by adopting the template obtaining module to divide so as to obtain a plurality of groups of aggregate effective areas, and each group of aggregate effective areas comprises I Xr 、I Yr And I Zr Wherein I Zr Representing an effective area obtained by dividing the corresponding z-channel depth map in the step2 by each template image, and I Xr Representing an effective area obtained by segmenting the original depth map of the corresponding aggregate in the step1 in the x channel by each template image, I Yr Representing an effective area obtained by dividing the original depth image of the corresponding aggregate in the step1 in the y channel by each template image;
the characteristic acquisition module is used for converting a plurality of groups of aggregate effective areas obtained by the aggregate effective area generation module into an aggregate point cloud model according to the PCL operator, wherein the aggregate point cloud model comprises a plurality of aggregate point clouds, and three-dimensional characteristics and two-dimensional characteristics of each aggregate point cloud are extracted;
the model training module is used for taking all three-dimensional features and two-dimensional features of the aggregate point cloud model obtained by the feature obtaining module as feature data sets, establishing an XGBoost model, taking the feature data sets obtained by the aggregate effective area generating module as input, taking the gear and quality of all aggregates as a label set, training the XGBoost model, and taking the trained XGBoost model as an aggregate grading rapid detection model;
the detection module is used for acquiring three-dimensional point cloud data of the aggregate to be detected, acquiring a characteristic data set of the three-dimensional point cloud data of the aggregate to be detected, inputting the characteristic data set of the three-dimensional point cloud data of the aggregate to be detected into the aggregate grading rapid detection model, and acquiring the gear and quality of the aggregate to be detected.
Specifically, the denoising in the denoising module includes the following steps:
1) Threshold segmentation is carried out on the z-channel depth map;
2) And converting the image subjected to threshold segmentation into a real height image according to the z-axis resolution, denoising the real height image by sequentially executing opening operation and closing operation, and obtaining a denoised z-channel depth image.
Specifically, the first threshold is 300 pixels.
Specifically, each aggregate point cloud in the aggregate point cloud model in the belonging feature acquisition module satisfies the following conditions:
1) The pixel distance between the eight neighborhood point sets is smaller than a second threshold, and the second threshold is 15 pixels;
2) The number of the connected point sets in each aggregate point cloud is larger than a third threshold, and the third threshold is 500 pixels.
Specifically, the data acquisition module adopts a Gocator2300 sensor module to be fixed with the conveyor belt and arranged right above the aggregate object stage, and adopts a smart200PLC control module to control a three-dimensional camera on the conveyor belt to scan aggregate and obtain point cloud data of the aggregate. And the step length of the conveyor belt and the acquisition resolution of the camera are synchronized by adopting an s7-200 encoder module so as to ensure that the acquired three-dimensional characteristic size accords with a true value.
Example 1
In this embodiment, a method for establishing an aggregate grading rapid detection model is disclosed, wherein the scale of establishing an aggregate characteristic data set is as follows: collecting 2200 Guangdongs and 2200 Gansu aggregates, and preparing 4.75, 9.6, 13.2 and 16mm four-grade aggregate three-dimensional characteristic data sets, wherein the data sets are stored in a csv format and comprise 52 three-dimensional characteristics, quality, gear and needle-shaped.
The specific labeling process when the label set is established is as follows, 4400 aggregates sequentially pass through 16, 13.2, 9.6 and 4.75mm particle-size square hole sieves specified in JTGE42-2005 highway engineering aggregate test procedure, the non-passers are labeled according to the current aperture, then needle-shaped identification is carried out on the aggregates according to the specified particle size by using a gauge, the non-needle-shaped particles with the particle length larger than the corresponding spacing on the needle-shaped gauge are selected as needle-shaped particles, the non-needle-shaped particles with the corresponding spacing passing through the needle-shaped gauge are subjected to sheet-shaped particle identification one by one, and the non-needle-shaped particles with the thickness smaller than the corresponding aperture width passing through the sheet-shaped gauge are selected as sheet-shaped particles. In this embodiment, the needles and the sheets are not distinguished, and are collectively labeled as needle sheets, and the data set is produced as shown in fig. 4.
As shown in fig. 1, which shows the denoising process of the present embodiment, it can be seen that the image quality has been significantly improved after 3 sets of opening and closing operations, the noise point has been substantially completely removed after all 7 sets of opening and closing operations, and there is substantially no information loss. Fig. 1 (a) is an original view, fig. 1 (b) is a first opening operation and the structural element size is 0.5, fig. 1 (c) is a first closing operation and the structural element size is 0.5, fig. 1 (d) is a second opening operation and the structural element size is 1.0, fig. 1 (e) is a second closing operation and the structural element size is 1.0, fig. 1 (f) is a third opening operation and the structural element size is 1.5, fig. 1 (g) is a third closing operation and the structural element size is 1.5, fig. 1 (h) is a fourth opening operation and the structural element size is 2.0, fig. 1 (i) is a fourth closing operation and the structural element size is 2.0, fig. 1 (j) is a fifth opening operation and the structural element size is 2.5, fig. 1 (k) is a sixth opening operation and the structural element size is 1.5, fig. 1 (h) is a fourth opening operation and the structural element size is 2.0, fig. 1 (m) is a seventh opening operation and the structural element size is 3.5, and the structural element size is 1.0.
As shown in FIG. 3, the comparison of the predicted grading and the actual grading in the embodiment can be seen that the method meets the error within 5%, and the calculation time is within 10 seconds, so that the method can meet the engineering requirement of rapid grading detection of aggregate compared with the existing grading manual detection method which needs a plurality of hours.
To illustrate the effectiveness of the method of the present invention, the inventors performed a rapid aggregate grading test based on three-dimensional feature factors. The operating system used was Win10 64 and the CPU was Intel (R) Core (TM) i5-9400 CPU@2.90GHz.
According to the invention, three-dimensional point cloud data of the background and the aggregate are continuously collected respectively for grading prediction. Separating three channels of the two depth images respectively, and then performing difference removal on the two images to remove the background; denoising through continuous opening and closing operation to obtain aggregate binary images with interference noise removed; then calculating and counting the connected domains and the number of the aggregates to be used as a three-channel effective area template; then converting the three-channel image into a 3D point cloud model, generating a template to extract a point cloud effective area, and dividing and communicating aggregate point clouds in the effective area; then three-dimensional characteristics of each aggregate are extracted respectively and input into a trained classification and regression model, and a prediction result of the quality and gear of each aggregate is obtained; finally, the grading is counted and obtained, and the calculation result is shown in fig. 5, wherein the first column is the aggregate gear, the second column is the aggregate ratio of different gears, the third column is the passing rate, and the fourth column is the screen residue rate.
Claims (9)
1. The method for establishing the aggregate grading rapid detection model is characterized by comprising the following steps of:
step1: acquiring three-dimensional point cloud data of multiple groups of aggregates and three-dimensional point cloud data of a background, and respectively carrying out channel separation on the three-dimensional point cloud data of each group of aggregates and the three-dimensional point cloud data of the background to acquire an original depth map of each group of aggregates in x, y and z channels and a depth map of the background in x, y and z channels;
each group of aggregate comprises at least one aggregate, and gears and quality of all aggregates are collected;
step2: subtracting the original depth map of each group of aggregate in the z channel from the depth map of the background in the z channel respectively to obtain a plurality of z channel depth maps, denoising the plurality of z channel depth maps to obtain a plurality of denoised z channel depth maps;
step3: calculating the area of the connected domain in each denoised z-channel depth map in the step2, and merging the connected domains with the area of the connected domain larger than a first threshold value in each denoised z-channel depth map into a template image to obtain a plurality of template images;
step 4: dividing the template images obtained in the step3 to obtain a plurality of groups of aggregate effective areas, wherein each group of aggregate effective areas comprises I Xr 、I Yr And I Zr Wherein I Zr Representing an effective area obtained by dividing the corresponding z-channel depth map in the step2 by each template image, and I Xr Representing an effective area obtained by segmenting the original depth map of the corresponding aggregate in the step1 in the x channel by each template image, I Yr Representing an effective area obtained by dividing the original depth image of the corresponding aggregate in the step1 in the y channel by each template image;
step 5: converting the multiple groups of aggregate effective areas obtained in the step 4 into aggregate point cloud models according to PCL operators, wherein the aggregate point cloud models comprise multiple aggregate point clouds, and extracting three-dimensional features and two-dimensional features of each aggregate point cloud;
step 6: and (3) taking all three-dimensional features and two-dimensional features of the aggregate point cloud model obtained in the step (5) as feature data sets, establishing an XGBoost model, taking the feature data sets obtained in the step (4) as input, taking the gear and quality of all aggregates obtained in the step (1) as a label set, training the XGBoost model, and taking the trained XGBoost model as an aggregate grading rapid detection model.
2. The method for building an aggregate gradation rapid inspection model according to claim 1, wherein the denoising in step2 comprises the steps of:
1) Threshold segmentation is carried out on the z-channel depth map;
2) And converting the image subjected to threshold segmentation into a real height image according to the z-axis resolution, denoising the real height image by sequentially executing opening operation and closing operation, and obtaining a denoised z-channel depth image.
3. The method for building an aggregate gradation rapid inspection model according to claim 1, wherein the first threshold value in the step3 is 300 pixels.
4. The method for building an aggregate gradation rapid inspection model according to claim 1, wherein each aggregate point cloud in the aggregate point cloud model of step 5 satisfies:
1) The pixel distance between the eight neighborhood point sets is smaller than a second threshold, and the second threshold is 15 pixels;
2) The number of the connected point sets in each aggregate point cloud is larger than a third threshold, and the third threshold is 500 pixels.
5. The rapid aggregate grading detection method is characterized by comprising the following steps of:
step I: collecting three-dimensional point cloud data of aggregate to be detected;
step II: acquiring a characteristic dataset of three-dimensional point cloud data of aggregate to be detected according to steps 1-5 of the method for establishing an aggregate grading rapid detection model according to any one of claims 1-4;
step III: inputting the characteristic data set of the three-dimensional point cloud data of the aggregate to be detected into the aggregate grading rapid detection model obtained by the method for establishing the aggregate grading rapid detection model according to any one of claims 1-4, and obtaining the gear and quality of the aggregate to be detected.
6. The aggregate grading rapid detection system is characterized by comprising a data acquisition module, a denoising module, a template acquisition module, an aggregate effective area generation module, a characteristic acquisition module, a model training module and a detection module;
the data acquisition module is used for acquiring three-dimensional point cloud data of a plurality of groups of aggregates and three-dimensional point cloud data of a background, and respectively carrying out channel separation on the three-dimensional point cloud data of each group of aggregates and the three-dimensional point cloud data of the background to acquire an original depth map of each group of aggregates in x, y and z channels and a depth map of the background in x, y and z channels; each group of aggregate comprises at least one aggregate, and gears and quality of all aggregates are collected;
the denoising module is used for subtracting the original depth map of each group of aggregate in the z channel, which is obtained by the data acquisition module, from the depth map of the background in the z channel respectively to obtain a plurality of z channel depth maps, denoising the plurality of z channel depth maps, and obtaining a plurality of denoised z channel depth maps;
the template acquisition module is used for calculating the area of the connected domain in each denoised z-channel depth map in the denoising module, and combining the connected domains with the area of the connected domain larger than a first threshold value in each denoised z-channel depth map into a template image to obtain a plurality of template images;
the aggregate effective area generating module is used for obtaining a plurality of template images by adopting the template obtaining module to divide so as to obtain a plurality of groups of aggregate effective areas, and each group of aggregate effective areas comprises I Xr 、I Yr And I Zr Wherein I Zr Representing an effective area obtained by dividing the corresponding z-channel depth map in the step2 by each template image, and I Xr Representing an effective area obtained by segmenting the original depth map of the corresponding aggregate in the step1 in the x channel by each template image, I Yr Representing an effective area obtained by dividing the original depth image of the corresponding aggregate in the step1 in the y channel by each template image;
the characteristic acquisition module is used for converting a plurality of groups of aggregate effective areas obtained by the aggregate effective area generation module into an aggregate point cloud model according to the PCL operator, wherein the aggregate point cloud model comprises a plurality of aggregate point clouds, and three-dimensional characteristics and two-dimensional characteristics of each aggregate point cloud are extracted;
the model training module is used for taking all three-dimensional features and two-dimensional features of the aggregate point cloud model obtained by the feature obtaining module as feature data sets, establishing an XGBoost model, taking the feature data sets obtained by the aggregate effective area generating module as input, taking the gear and quality of all aggregates as a label set, training the XGBoost model, and taking the trained XGBoost model as an aggregate grading rapid detection model;
the detection module is used for acquiring three-dimensional point cloud data of the aggregate to be detected, acquiring a characteristic data set of the three-dimensional point cloud data of the aggregate to be detected, inputting the characteristic data set of the three-dimensional point cloud data of the aggregate to be detected into the aggregate grading rapid detection model, and acquiring the gear and quality of the aggregate to be detected.
7. The aggregate gradation rapid detection system of claim 6, wherein the denoising in the denoising module comprises the steps of:
1) Threshold segmentation is carried out on the z-channel depth map;
2) And converting the image subjected to threshold segmentation into a real height image according to the z-axis resolution, denoising the real height image by sequentially executing opening operation and closing operation, and obtaining a denoised z-channel depth image.
8. The rapid aggregate gradation detection system of claim 6, wherein the first threshold is 300 pixels.
9. The rapid aggregate grading detection system according to claim 6, wherein each aggregate point cloud in the aggregate point cloud model in the belonging feature acquisition module satisfies:
1) The pixel distance between the eight neighborhood point sets is smaller than a second threshold, and the second threshold is 15 pixels;
2) The number of the connected point sets in each aggregate point cloud is larger than a third threshold, and the third threshold is 500 pixels.
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Application publication date: 20210611 Assignee: Shaanxi Xinglang Keao Network Technology Co.,Ltd. Assignor: CHANG'AN University Contract record no.: X2023980049357 Denomination of invention: Establishment, Detection Method and System of a Rapid Detection Model for Aggregate Grading Granted publication date: 20230804 License type: Common License Record date: 20231205 |