CN111026902A - Intelligent identification system and method for building material category - Google Patents
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Abstract
The invention relates to the field of intelligent identification, in particular to an intelligent identification system and method for building material categories, which comprises the following steps: the acquisition module is used for acquiring the image of the building material; the preprocessing module is used for preprocessing the acquired image; the extraction module is used for extracting the texture features of the preprocessed image; the identification module is used for identifying according to the matching of the extracted texture features and the database; and the output module is used for outputting the recognition result. By extracting the texture features, the method reduces the influence of illumination in the building material identification process and improves the identification effect under the condition of severe illumination change.
Description
Technical Field
The invention relates to the field of intelligent identification, in particular to an intelligent identification system and method for building material categories.
Background
At present, real estate is developed vigorously, and the demand for building materials is also increased dramatically. In order to guarantee the supply and sale of building materials, large building material enterprises are equipped with large enough warehouses. A large building material warehouse stores various building materials, managers are difficult to keep pace with the production speed of enterprises by adopting manual inspection and classification, and great working pressure is brought to workers. In view of the above, document CN108229370A discloses an intelligent warehouse cargo identification system based on cargo image identification technology, which includes a data input module, a data storage module, an analysis and comparison module, a phase acquisition module, an output module, and an anomaly alarm module. The method compares the image of the good product sample with the image of the actual goods by the image recognition technology to judge whether the detected goods are correct good products, and has high accuracy and higher detection speed.
However, image recognition requires collecting pictures of building materials, the quality of the collected pictures is affected by light and environment, and the quality of the pictures directly affects the accuracy of recognition. Therefore, the building materials are difficult to accurately identify in environments with poor lighting conditions.
Disclosure of Invention
The invention provides an intelligent identification system and method for building material categories, which solve the technical problem that the existing cargo image identification system is difficult to accurately identify building materials in environments with poor illumination conditions.
The basic scheme provided by the invention is as follows: an intelligent identification system for a category of construction materials, comprising: the acquisition module is used for acquiring the image of the building material; the preprocessing module is used for preprocessing the acquired image; the extraction module is used for extracting the texture features of the preprocessed image; the identification module is used for identifying according to the matching of the extracted texture features and the database; and the output module is used for outputting the recognition result.
The texture features of the building material are extracted when the building material is identified, and graying and binarization processing are carried out when the texture is extracted; the influence of illumination in the building material identification process is reduced, and the identification effect under the condition of severe illumination change is improved.
The intelligent identification system for the building material category identifies the building material by matching the textural features in the image, rather than by matching the entire image. The influence of the illumination condition on the recognition result is greatly eliminated, and the recognition accuracy is improved.
Further, a storage module is included for storing the matching and recognition results. Learning samples are provided for matching of textural features so that the system can adaptively analyze new samples of building materials, thereby improving the accuracy of identification.
Further, the system comprises a learning module for learning according to the history matching and the recognition result. Optimizing matching efficiency and accuracy through learning; the matching error can be corrected, and the identification accuracy is improved.
And further, the device comprises a feedback module, and when the matching error is larger than a preset threshold value, the matching is carried out again until the error is smaller than the preset threshold value. And the matching result is fed back, so that the matching process is continuously corrected, and the identification accuracy can be improved.
Further, the method comprises a clustering module, wherein the extracted textural features are classified by adopting a k-means clustering algorithm; the method comprises the following specific steps: s11: inputting texture features; s12: randomly selecting K texture features as initial clustering centers; s13: assigning each object to the cluster center closest to it; s14: recalculating the clustering center; s15: if the convergence is achieved, outputting a clustering result; if not, the process returns to step S12. The extracted texture features are classified before matching and recognition, so that matching is performed in a certain category, and the probability of matching errors can be reduced.
Further, extracting texture features and adopting LBP features; the method comprises the following specific steps: s21: obtaining a basic local binary pattern of each pixel of the window; s22: the realization of invariable rotation: circularly right-shifting the binary string to obtain all possible values, and taking the minimum value as the LBP value of the current window; s23: reducing feature dimensions by using an equivalent mode; s24: obtaining a histogram of the whole window by using the LBP value of each pixel; s25: judging whether to traverse the whole image, and if not: step S21 is returned to after the step moving window is connected; if yes, go to step S26; s26: connecting the histograms of all the windows to obtain LBP characteristics under the window scale, and scaling the window according to the window scaling factor; s27, judging whether the maximum window upper limit is exceeded, if yes: outputting a result; otherwise: returning to step S21, the image is traversed from the beginning using the new window size. LBP (Local Binary Pattern) is an operator for describing Local texture characteristics of an image, has rotation invariance and gray scale invariance, and has a good identification effect under the condition of severe illumination change.
Further, a sequential similarity detection algorithm is adopted during matching; the method comprises the following specific steps: s31: selecting an error criterion; s32: setting a constant threshold value; s33: randomly selecting a point from the sub-image, calculating the absolute error value between the point and the corresponding point in the template, accumulating the error of each random point pair, stopping accumulation if the error exceeds a set threshold value when the error is accumulated for the r-th time, and recording the accumulated time r; s34: and calculating the error e of the whole image to obtain a curved surface consisting of r values, wherein the position corresponding to the maximum value of the curved surface is the best matching position of the template. The sequential similarity detection algorithm looks for corresponding or similar modules in another image (the search graph) based on known image modules (the template graph). The initial search is carried out firstly, then the fine search is carried out, the search range is gradually reduced, and the identification accuracy is greatly improved.
Further, the preprocessing module adopts a Gaussian filtering algorithm. Most of noise of the image belongs to Gaussian noise, the algorithm is used for carrying out weighted average on the whole image, the value of each pixel point is obtained by carrying out weighted average on the value of each pixel point and other pixel values in the neighborhood, and the algorithm is suitable for eliminating the Gaussian noise.
Further, the acquisition module is a CMOS image sensor. The sensor has low power consumption, high imaging speed and wide response range, and is favorable for quickly acquiring images of building materials.
Drawings
Fig. 1 is a block diagram of a system structure of an embodiment of the intelligent identification system and method for building material categories according to the invention.
Detailed Description
The following is further detailed by the specific embodiments:
example 1
An embodiment of the intelligent identification system for building material categories according to the present invention is substantially as shown in fig. 1, and comprises: the device comprises an acquisition module, a preprocessing module, an extraction module, a clustering module, an identification module, a feedback module, a storage module, a learning module and an output module.
The acquisition module is a CMOS image sensor which acquires the texture on the building material and sends the image to the preprocessing module. And after receiving the image, the preprocessing module denoises the image by adopting a Gaussian filtering algorithm and sends the denoised image to the feature extraction module.
And after receiving the image, the feature extraction module extracts the LBP texture feature of the image. The method comprises the following specific steps: step one, a basic local binary pattern of each pixel of a window is obtained. And step two, circularly right-shifting the binary string to obtain all possible values, and taking the minimum value as the LBP value of the current window, thereby realizing rotation invariance. And step three, reducing the characteristic dimension by using the equivalent mode. And step four, obtaining a histogram of the whole window by using the LBP value of each pixel. Step five, judging whether to traverse the whole image: if not, connecting the step length moving window and returning to the step one; if yes, executing step six. And step six, connecting the histograms of all the windows to obtain the LBP characteristics under the window scale, and scaling the window according to the window scaling factor. Step seven, judging whether the maximum window upper limit is exceeded: if yes, outputting a result; if not: return to step one to traverse the image from scratch using the new window size. And after the extraction of the textural features of the image is finished, sending the textural features to a clustering module.
And after receiving the texture features, the clustering module classifies the extracted texture features by adopting a k-means clustering algorithm. The method comprises the following specific steps: step one, inputting texture features. And step two, randomly selecting K texture features as initial clustering centers. And step three, each object is assigned to the nearest cluster center. And step four, recalculating the clustering center. Step five, if convergence occurs, a clustering result is output; if not, returning to the step two. And finally, the clustering module classifies the texture features into a certain class according to the similarity of the texture features, and sends the classification result to the identification module.
And the identification module is used for matching the texture features with the pre-stored texture features in the database of the category according to the category of the texture features. When in matching, a sequential similarity detection algorithm is adopted, and a corresponding or similar module is searched in another image (a search graph) according to a known image module (a template graph); the initial search is firstly carried out, then the fine search is carried out, and the search range is gradually reduced. The method comprises the following specific steps: step one, selecting an error criterion. If the matching error does not exceed 5%, the method is used as a standard for terminating the calculation of the mismatch point. And step two, setting a constant threshold value, such as 10%. And step three, randomly selecting a point in the sub-image, calculating the absolute error value between the point and the corresponding point in the template, accumulating the error of each random point pair, stopping accumulation if the error exceeds a set threshold value when the error is accumulated for the r-th time, and recording the accumulated time r at the moment. And step four, calculating an error e for the whole image to obtain a curved surface formed by the r value, wherein the position corresponding to the maximum value of the curved surface is the best matching position of the template. And after matching is completed, sending the recognition result to an output module, and outputting the recognition result to a display screen by the output module.
Example 2
Compared with the embodiment 1, the difference is only that: the device also comprises a storage module, a learning module and a feedback module. The storage module is used for storing the matching and recognition results and providing learning samples for the matching of the texture features, so that the system can adaptively analyze new samples of the building materials, and the recognition accuracy is improved. The learning module extracts the matching and recognition results in the storage module to learn, and optimizes matching efficiency and accuracy. Through learning, the matching error is corrected, and the identification accuracy is improved. The feedback module corrects the matching process through negative feedback. And when the matching error is larger than the preset threshold value, the matching is carried out again until the error is smaller than the preset threshold value. Thus, the matching result is fed back, so that the matching process is continuously corrected, and the identification accuracy can be improved.
Example 3
Compared with the embodiment 1, the difference is only that: also included are a pressure sensor and a 3D camera. The pressure sensor is placed under the construction material and connected to the identification module via a wireless network. The pressure sensor is used for measuring the weight of the building material and transmitting the measured weight data of the building material to the identification module through a wireless network. The 3D camera is installed in a warehouse for storing building materials and is connected with the identification module through a wireless network. The 3D camera is used for photographing the length, width and height of the building material, and sending the photographed length, width and height size data of the building material to the identification module (refer to "calibration algorithm of high precision camera in 3D measurement system"). In addition, a pressure sensor is mounted on the vehicle, which acquires the time profile of the total weight of the vehicle and the transported goods and transmits the profile to the identification module.
When the identification module receives the length, width and height dimension data and the weight data of the building material, the volume data of the building material is calculated according to the length, width and height dimension data, and then the density value of the building material is calculated according to the weight data and the volume data. And finally, matching according to the density value, and identifying the type of the building material. Because the density difference of different building materials is large, the accuracy rate is greatly improved by identifying the types of the building materials through matching the density values.
Meanwhile, the identification module can calculate the change curve of the density value along with the time by combining the change curve of the total weight along with the time with the volume data. Because the warehouse entry and exit department all is equipped with the speed reduction bank basically, also can appear the condition that the driver brakes or starts simultaneously. Due to the effect of the inertia force, the total weight of the transport vehicle and the transported goods acquired by the pressure sensor fluctuates with time to a certain extent.
The density values calculated according to the formula vibration mechanics in terms of amplitude therefore also have a certain fluctuation in the course of time, but this amplitude is usually small, for example within 0.2%. When the identification module detects that the peak of the density value in the oscillation state exceeds a preset threshold (such as 0.5%), the density value fluctuation is not caused by inertia force, and people privately unload the goods or drop the goods in the warehouse.
If the result identified through the textural features is different from the result identified through the density value, or the peak of the density value in the oscillation state is detected to exceed a preset threshold value, the identification module informs of risks and prompts that the type of the building material has a problem. The building material may be fraudulently filled or may be unpacked. In fact, it is not uncommon for building materials to be depacketized and be subject to a fake news story.
Therefore, the identification is carried out through the textural features and the density value, and whether the wave crest of the density value in the oscillation state exceeds the preset threshold value is detected, so that the identification accuracy of the building material can be improved, and the building material can be effectively prevented from being falsely filled and falling.
The foregoing is merely an example of the present invention, and common general knowledge in the field of known specific structures and characteristics is not described herein in any greater extent than that known in the art at the filing date or prior to the priority date of the application, so that those skilled in the art can now appreciate that all of the above-described techniques in this field and have the ability to apply routine experimentation before this date can be combined with one or more of the present teachings to complete and implement the present invention, and that certain typical known structures or known methods do not pose any impediments to the implementation of the present invention by those skilled in the art. It should be noted that, for those skilled in the art, without departing from the structure of the present invention, several changes and modifications can be made, which should also be regarded as the protection scope of the present invention, and these will not affect the effect of the implementation of the present invention and the practicability of the patent. The scope of the claims of the present application shall be determined by the contents of the claims, and the description of the embodiments and the like in the specification shall be used to explain the contents of the claims.
Claims (10)
1. An intelligent identification system for building material classes, characterized by: the method comprises the following steps:
the acquisition module is used for acquiring the image of the building material;
the preprocessing module is used for preprocessing the acquired image;
the extraction module is used for extracting the texture features of the preprocessed image;
the identification module is used for identifying according to the matching of the extracted texture features and the database;
and the output module is used for outputting the recognition result.
2. The intelligent identification system for building material classes according to claim 1, characterized in that: the device comprises a storage module used for storing the matching and recognition results.
3. The intelligent identification system for building material classes according to claim 2, characterized in that: the learning module is used for learning according to historical matching and recognition results.
4. The intelligent identification system for building material classes according to claim 3, characterized in that: the method comprises a feedback module, and when the matching error is larger than a preset threshold value, the matching is carried out again until the error is smaller than the preset threshold value.
5. The intelligent identification system for building material classes according to claim 4, characterized in that: the method comprises a clustering module, a texture feature extraction module and a texture feature extraction module, wherein the clustering module is used for classifying extracted texture features by adopting a k-means clustering algorithm; the method comprises the following specific steps: s11: inputting texture features; s12: randomly selecting K texture features as initial clustering centers; s13: assigning each object to the cluster center closest to it; s14: recalculating the clustering center; s15: if the convergence is achieved, outputting a clustering result; if not, the process returns to step S12.
6. The intelligent identification system for building material classes according to claim 5, characterized in that: extracting texture features and adopting LBP features; the method comprises the following specific steps: s21: obtaining a basic local binary pattern of each pixel of the window; s22: the realization of invariable rotation: circularly right-shifting the binary string to obtain all possible values, and taking the minimum value as the LBP value of the current window; s23: reducing feature dimensions by using an equivalent mode; s24: obtaining a histogram of the whole window by using the LBP value of each pixel; s25: judging whether to traverse the whole image, and if not: step S21 is returned to after the step moving window is connected; if yes, go to step S26; s26: connecting the histograms of all the windows to obtain LBP characteristics under the window scale, and scaling the windows according to the window scaling factor; s27: judging whether the maximum window upper limit is exceeded, if so, judging that: outputting a result; otherwise: returning to step S21, the image is traversed from the beginning using the new window size.
7. The intelligent identification system for building material classes according to claim 6, characterized in that: a sequential similarity detection algorithm is adopted during matching; the method comprises the following specific steps: s31: selecting an error criterion; s32: setting a constant threshold value; s33: randomly selecting a point from the sub-image, calculating the absolute error value between the point and the corresponding point in the template, accumulating the error of each random point pair, stopping accumulation if the error exceeds a set threshold value when the error is accumulated for the r-th time, and recording the accumulated time r; s34: and calculating the error e of the whole image to obtain a curved surface consisting of r values, wherein the position corresponding to the maximum value of the curved surface is the best matching position of the template.
8. The intelligent identification system for building material classes according to claim 7, characterized in that: the preprocessing module adopts a Gaussian filtering algorithm.
9. The intelligent identification system for building material classes according to claim 8, characterized in that: the acquisition module is a CMOS image sensor.
10. An intelligent identification system for building material classes, characterized by: the method comprises the following steps:
s01: acquiring an image of a building material;
s02: preprocessing the acquired image, including graying and binaryzation;
s03: extracting texture features;
s04: clustering the texture features;
s05: matching the extracted texture features with a database, and identifying;
s06: judging whether the matching error is larger than a preset threshold value or not, and when the matching error is larger than the preset threshold value, performing matching again until the error is smaller than the preset threshold value;
s07: storing the matching and identifying results;
s08: learning according to the stored matching and recognition results;
s09: and outputting the recognition result.
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Address after: Room 2502, Building 3, Xingyi, Jushan Street, Xingyi City, Qianxinan Buyi and Miao Autonomous Prefecture, Guizhou Province, 562400 Patentee after: Guizhou Fantian Technology Co.,Ltd. Address before: 562400 room 602, unit 2, building 25, feiyanghuafu, Fengdu sub district office, Xingyi City, Qianxinan Buyei and Miao Autonomous Prefecture, Guizhou Province Patentee before: Guizhou Qianan Technology Co.,Ltd. |