Code recognition method, device and equipment for reinforcing steel bars and storage medium

Abstract

The invention discloses a code recognition method and device for a reinforcing steel bar, electronic equipment and a storage medium, and the method comprises the steps: carrying out the image collection through an image collection device when the reinforcing steel bar is detected to reach a detection position, and obtaining an end face image of the reinforcing steel bar; identifying and positioning the end face picture to obtain a reinforcing steel bar end face picture of the reinforcing steel bar end face contained in the end face picture; identifying a coding area of the steel bar end face picture to obtain the coding area contained in the steel bar end face picture; and character string recognition processing is conducted on the coding area, and a coding character string of the reinforcing steel bar is obtained. According to the method, information such as the end face picture of the reinforcing steel bar is obtained for analysis and processing, the coding information of the reinforcing steel bar can be accurately and rapidly identified and obtained, and the management convenience and the use traceability of the reinforcing steel bar in the construction process are effectively improved.

Description

Technical Field

[0001] The present application relates to the field of electromechanical engineering technology, and in particular to a coding identification method, device, electronic equipment and storage medium for steel bar materials. Background Art

[0002] At present, during the construction of houses, steel bars (diameter φ16mm and above) are used in large quantities in different parts of the house. Due to the increase in demand for steel bars, and the fact that they are mainly sorted and managed manually during the control of steel bars, quality accidents such as wrong parts or missed inspections may occur, resulting in low efficiency in the control of steel bars, and the inability to trace the use of steel bars. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a coding identification method, device, electronic device and storage medium for steel bars, so as to solve the technical problems in the related art of low control efficiency and inability to trace steel bars.

[0004] In a first aspect, an embodiment of the present application provides a coding identification method for a steel bar, comprising:

[0005] When it is detected that the steel bar reaches the detection position, an image acquisition device is used to acquire an image of the end face of the steel bar;

[0006] Identify and locate the end face image to obtain a steel bar end face image of the steel bar end face contained in the end face image;

[0007] Performing coding area recognition processing on the steel bar end face image to obtain the coding area contained in the steel bar end face image;

[0008] The coding region is subjected to character string recognition processing to obtain the coding character string of the steel bar.

[0009] In a second aspect, an embodiment of the present application provides a coding identification device for a steel bar, comprising:

[0010] An image acquisition module is used to acquire an image by using an image acquisition device to obtain an end face image of the steel bar when it is detected that the steel bar reaches the detection position;

[0011] A first processing module is used to identify and locate the end face image to obtain a steel bar end face image of the steel bar end face contained in the end face image;

[0012] A second processing module is used to perform a coding area recognition process on the steel bar end face image to obtain the coding area contained in the steel bar end face image;

[0013] The recognition processing module is used to perform character string recognition processing on the coding area to obtain the coding character string of the steel bar.

[0014] In a third aspect, an embodiment of the present application provides an electronic device, which includes a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the steps in any one of the above-mentioned methods for coding and identifying steel bars when executing the computer program.

[0015] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the coding identification method for steel bars described in any one of the above items are implemented.

[0016] The embodiment of the present application provides a coding identification method, device, electronic device and storage medium for steel bar. In the process of identifying and processing the coding information of steel bar, when the steel bar is detected to reach the detection position, an image acquisition device is used to collect images to obtain the end face picture of the steel bar, and then the end face picture is identified and positioned to obtain the steel bar end face picture of the steel bar end face contained in the end face picture, and then the coding area in the steel bar end face picture is identified, and finally the characters contained in the coding area are identified to obtain the coding string of the steel bar. It is realized that by obtaining the end face picture of the steel bar and other information for analysis and processing, the coding information of the steel bar can be accurately and quickly identified and obtained, which effectively improves the management convenience and use traceability of the steel bar material during the construction process. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 It is a flow chart of a coding identification method for steel bars provided in an embodiment of the present application;

[0018] Figure 2 It is a hardware structure diagram of the code recognition device provided in the embodiment of the present application;

[0019] Figure 3 This is a schematic diagram of a process for character marking of steel bars provided in an embodiment of the present application;

[0020] Figure 4 It is a hardware structure diagram of the steel bar marking device provided in the embodiment of the present application;

[0021] Figure 5 This is a schematic diagram of a process for processing an end face image provided by an embodiment of the present application;

[0022] Figure 6This is a flowchart of the steps of obtaining a coded string provided in an embodiment of the present application;

[0023] Figure 7 This is a schematic diagram of a process for post-processing a coded string provided in an embodiment of the present application;

[0024] Figure 8 It is a structural schematic diagram of a coding identification device for steel bars provided in an embodiment of the present application;

[0025] Fig. 9 is a structural schematic diagram of an electronic device provided in an embodiment of the present application;

[0026] Fig.10 This is another structural schematic diagram of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0028] It should be understood that the various steps described in the method embodiments of the present disclosure may be performed in different orders and / or in parallel. In addition, the method embodiments may include additional steps and / or omit the steps shown. The scope of the present disclosure is not limited in this respect.

[0029] The term "including" and its variations used herein are open inclusions, i.e., "including but not limited to". The term "based on" means "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment"; the term "some embodiments" means "at least some embodiments". The relevant definitions of other terms will be given in the following description.

[0030] In the related art, the control of steel bars is mainly carried out through manual sorting and management, which may lead to quality accidents such as wrong pieces or missed inspections of steel bars, resulting in low efficiency in the control of steel bars and the inability to trace the use of steel bars.

[0031] In order to solve the technical problems existing in the related art, the embodiment of the present application provides a coding identification method for steel bars, see Figure 1 , Figure 1It is a flow chart of a coding identification method for steel bars provided in an embodiment of the present application, and the method includes steps 101 to 104.

[0032] Step 101, when it is detected that the steel bar arrives at the detection position, an image acquisition device is used to acquire an image of the end face of the steel bar.

[0033] In one embodiment, when analyzing and processing the steel bar to determine the model and other information of the steel bar, specifically, by acquiring relevant images of the steel bar, the coding information of the steel bar is obtained through analysis and processing, wherein the coding information is the model and other information of the steel bar. Therefore, during the processing, when it is detected that the steel bar reaches the detection position, the image acquisition device is used to shoot and collect images to obtain the end face picture of the steel bar.

[0034] For example, when the steel bar is identified, the corresponding identification device and system are used for processing, referring to Figure 2 , Figure 2 It is a hardware structure diagram of the coding identification device provided in an embodiment of the present application, wherein the coding identification device includes an automatic bending machine for bar steel bars, an image acquisition device and a light source. The automatic bending machine for bar steel bars can fix and move the bar steel bars. When the bar steel bars are moved to the set position, the control system of the automatic bending machine for bar steel bars controls the light source and the image acquisition device to turn on, and then uses the image acquisition device to collect and process the image to obtain an end face picture of the bar steel bar, and then analyzes and processes the end face picture to obtain the coding string of the identified bar steel bar, wherein the coding string can be obtained by a combination of letters and numbers.

[0035] Furthermore, before the code of the steel bar is identified, the steel bar is marked in advance, and the code string corresponding to the steel bar is marked on the steel bar, and each type of steel bar has a unique code string, specifically marked on the end face of the steel bar. Of course, in the actual processing process, the code string can also be marked on other specific positions on the steel bar. Figure 3 , Figure 3 It is a flow chart of character marking of steel bars provided in an embodiment of the present application, wherein the step includes steps 301 to 303.

[0036] Step 301, when receiving a coding marking instruction, obtaining a marking string contained in the coding marking instruction;

[0037] Step 302, controlling the steel bar to be marked to move to a standard position, and pre-processing the area to be marked of the steel bar to be marked;

[0038] Step 303, determining a character border in the pre-processed area to be marked, and performing marking processing on the character border based on the marking character string to obtain a steel bar to be marked after the marking processing.

[0039] Specifically, when marking the steel bar, the steel bar is processed by using the steel bar marking device, referring to Figure 4 , Figure 4 It is a hardware structure schematic diagram of the steel bar marking device provided in an embodiment of the present application, wherein the steel bar marking device includes a marking roller and a laser marking device. When marking, the steel bar moves on the marking roller, and the roller on the marking roller drives the steel bar to move, and moves toward the direction of the laser marking machine. The position of the end face of the steel bar is determined by the mechanical limit baffle on the marking roller, so that after the steel bar is in place, the limit limit baffle is controlled to be lowered, and the laser marking machine is started at the same time to control the laser marking device to engrave the steel bar code on the end face of the steel bar.

[0040] Exemplarily, when processing is performed, when it is determined to perform coding and marking processing, a coding and marking instruction is received, and the marking character string contained in the coding and marking instruction is obtained, and then the steel bar to be marked is controlled to move to the standard position, and the area to be marked of the steel bar to be marked is preprocessed, wherein the preprocessing is to eliminate the noise generated by the sawing marks in the area to be marked, so as to facilitate clear marking processing in the area to be marked, and finally, after completing the preprocessing of the area to be marked, the character border to be marked is determined, and then the marking processing is performed in the character border according to the identified marking character string.

[0041] In practical applications, when marking steel bars, they can be processed according to pre-set marking rules. For example, the encoding string of the steel bar can be set to consist of 6 characters, which are divided into the first character, the middle character and the last character. The first character can be used to represent the construction project code, which consists of uppercase letters, excluding letters that may cause confusion, a total of 12 uppercase letters, including: A, B, D, F, G, K, P, R, T, U, V, Y; the middle character can represent the unique code of the steel bar, which consists of numbers / uppercase letters, excluding letters and numbers that may cause confusion, a total of 17 uppercase letters and 8 numbers, the letters include: A, B, C, D, F, G, H, K, N, P, R, S, T, U, V, X, Y, the numbers include: 0, 1, 2, 5, 6, 7, 8, 9; the last character can be used to represent the steel component package identification number, which consists of numbers, excluding numbers that may cause confusion, a total of 8 numbers, including: 0, 1, 2, 5, 6, 7, 8, 9. In addition, in the same reinforcement component package, the last character is the same number, and at least 2 of the middle characters are different.

[0042] Step 102, identifying and locating the end face image, and obtaining a steel bar end face image of the steel bar end face contained in the end face image.

[0043] In one embodiment, after the end face image of the steel bar is obtained by using an image acquisition device, the end face image is identified and positioned to determine the steel bar end face image contained in the end face image, and then through subsequent processing, the encoding string of the steel bar is determined in the steel bar end face image.

[0044] Exemplarily, after the end face picture of the steel bar is obtained by photographing the steel bar, the coded string of the steel bar contained in the end face picture is determined to determine the relevant information of the steel bar. When the end face picture is obtained by using an image acquisition device to acquire an image, the obtained end face picture contains not only the end face of the steel bar, but also other noise information. In order to improve the accuracy of the coded string recognition, the end face picture can be processed to remove the noise information in the end face picture, and the characteristics of the coded string in the end face picture can also be improved.

[0045] Further, refer to Figure 5 , Figure 5 It is a flow chart of processing an end face image provided in an embodiment of the present application, wherein the step includes steps 501 to 503.

[0046] Step 501, grayscale processing is performed on the end face image to obtain a grayscale image corresponding to the end face image;

[0047] Step 502, performing Gaussian blur processing on the grayscale image to obtain a grayscale image after Gaussian blur processing;

[0048] Step 503, identifying texture features of the grayscale image after Gaussian processing, obtaining a first texture feature in the steel bar, and obtaining a steel bar end face image of the steel bar end face contained in the end face image based on the bar edge feature in the first texture feature.

[0049] Specifically, when processing the end face image, the end face image is grayscale processed to obtain a grayscale image of the end face image, and then the grayscale image is Gaussian blurred to obtain a grayscale image after Gaussian processing. Finally, the texture features of the grayscale image after Gaussian processing are identified to obtain the first texture feature in the steel bar, and then the area of ​​the steel bar end face in the grayscale image after Gaussian processing is determined based on the edge feature of the bar in the first texture feature obtained, and then the steel bar end face image of the steel bar end face contained in the end face image can be obtained.

[0050] Exemplarily, after the end face image is grayscale processed, the RGB color information in the end face image can be converted into a single brightness information, thereby highlighting the character area in the end face image, and specifically, effectively highlighting the coded string. After completing the grayscale processing of the end face image, Gaussian blur processing is performed on the grayscale image after grayscale processing, and the noise and detail smoothing in the grayscale image are reduced by weighted averaging, thereby better facilitating the subsequent recognition of the coded string.

[0051] After obtaining the grayscale image after Gaussian processing, the texture features of the grayscale image after Gaussian processing are identified to obtain the first texture feature in the steel bar, and then the template matching and edge algorithm are combined with the example feature to obtain the steel bar end face image of the steel bar end face contained in the end face image, that is, to obtain image information containing only the steel bar end face, and then the encoding string of the steel bar is obtained by processing and identifying the image information.

[0052] Step 103, performing identification processing on the coding area of ​​the steel bar end face image to obtain the coding area contained in the steel bar end face image.

[0053] In one embodiment, after obtaining the steel bar end face picture, the coding area in the steel bar end face picture is identified to obtain the coding area contained in the steel bar end face picture. Specifically, when the coding area in the steel bar end face picture is identified, the texture features in the steel bar end face picture are identified to obtain the second texture features in the steel bar end face picture, wherein the second texture features include the coding area texture features and the coding character texture features. Therefore, when determining the coding area in the steel bar end face picture, the coding area in the steel bar end face picture is determined by identifying the regional edge features in the identified second texture features.

[0054] Exemplarily, when performing identification processing on the coding area of ​​the steel bar end face image, it includes: performing texture feature identification processing on the steel bar end face image to obtain a second texture feature in the steel bar end face image; obtaining a regional edge feature in the second texture feature according to the regional characteristics, and obtaining the coding area in the steel bar end face image based on the regional edge feature.

[0055] When processing, the regional edge features can be obtained in the second texture features according to the regional characteristics of the coding area to determine the coding area in the steel bar end face image. In practical applications, when laser marking steel bars, the coding string can be framed or limited to a region, such as a square region, and the coding string is within the square region. Based on this method, the regional characteristics of the coding area are continuous, that is, for the obtained steel bar end face image, it contains a square area and several characters. By identifying the square area, the coding area in the steel bar end face image is determined. Of course, in practical applications, the regional characteristics of the coding area are determined by the actual setting method. For example, the coding area can also be a circular area, and there is no specific restriction.

[0056] Step 104, performing character string recognition processing on the coding area to obtain the coding character string of the steel bar.

[0057] In one embodiment, after the coding region is determined by performing recognition processing on the steel bar image, the coding region is recognized and processed to obtain the coding string of the steel bar. Specifically, when the coding region in the end face image of the steel bar is recognized and processed, a pre-trained deep learning model can be used for processing, and the obtained coding region is input into the deep learning model for processing to output the coding string contained in the coding region.

[0058] Furthermore, when performing string recognition processing on the coding area, you can refer to Figure 6 , Figure 6 It is a flowchart of the steps of obtaining a coded string provided in an embodiment of the present application, wherein the steps include steps 601 to 604.

[0059] Step 601, processing the coding area based on the projection segmentation method to obtain a projection image corresponding to the coding area;

[0060] Step 602, performing character segmentation according to the projection image to obtain the character area included in the coding area;

[0061] Step 603, processing the character area to obtain the character corresponding to the character area;

[0062] Step 604, performing splicing processing according to the characters to obtain a coding string of the steel bar.

[0063] Specifically, when the coding area is identified, the coding area is processed based on the projection segmentation method to obtain a projection image corresponding to the coding area, and then character segmentation is performed based on the obtained projection image to obtain the character area contained in the coding area. Then, the character area is processed to obtain the characters contained in the character area. Finally, all the obtained characters are spliced ​​to obtain the coding string of the steel bar.

[0064] Exemplarily, when processing to obtain a coded string, it can be processed based on a pre-trained deep learning model as described above, and a projection image can be obtained by performing projection segmentation processing on the coded area, wherein the projection image can determine which areas contain characters, and by performing segmentation processing on the projection image, a number of character areas containing characters are obtained, and then each character area is predicted using a deep learning model to obtain the characters corresponding to each character area, and finally the characters corresponding to each character area are spliced ​​through splicing processing to obtain the coded string based on the deep learning model.

[0065] In practical applications, when the code string of the steel bar is identified, the corresponding original picture is first obtained by using a relevant image acquisition device, and then the code string of the steel bar is obtained by analyzing and identifying the obtained original picture. When processing the original picture, the following steps may be included:

[0066] Step 1: Grayscale and Gaussian blur the original image.

[0067] After the original image is grayed, the color information of the original image will be converted into a single brightness value, and then Gaussian blur (Gaussian smoothing) processing will be performed. The noise and details in the grayed image will be reduced through weighted averaging, and the smoothness of the grayscale image will be improved to obtain a gray and blurred image, laying the foundation for further image analysis and recognition.

[0068] Step 2: Rough positioning of steel bars.

[0069] By identifying the color, shape and texture features in the image (original image and grayscale image), combined with template matching and edge algorithm, the circular area of ​​the steel bar end surface in the image is determined.

[0070] Step 3: Precise positioning and correction of the steel bar coding area.

[0071] In the process of processing, the precise positioning and correction of the steel bar coding area is a key step. The image recognition technology can be used to accurately locate the steel bar coding area and perform distortion correction on the located area to ensure the clarity and readability of the code and the accuracy of subsequent identification.

[0072] Step 4: Intercept the steel bar coding area.

[0073] By using the image cropping technology to accurately extract the area containing the coding information from the original image through the steel bar coding area accurately located in the previous step, redundant information can be effectively removed, thus improving the efficiency and accuracy of subsequent coding recognition.

[0074] Step 5: Rotation correction of steel bar coding image and segmentation of coding characters.

[0075] For tilted or skewed coded images, accurate rotation correction can be performed according to the actual tilt angle of the original image to ensure the image is level. At the same time, for the corrected original image, the projection segmentation method can be used to effectively segment the characters by identifying the pixel distribution characteristics between characters to obtain clear and independent character images.

[0076] Step 6: Adaptively binarize and histogram equalize the characters.

[0077] Through adaptive binarization methods, such as local mean processing or local Gaussian processing, the threshold is automatically adjusted to convert the character image into a black and white binary image to highlight the outline and features of the characters. Then, the histogram equalization technology is applied to enhance the contrast of the character image by redistributing the grayscale values ​​of the image, making the distinction between the characters and the background more obvious, so as to improve the quality of the character image.

[0078] Step 7: Image normalization.

[0079] Before inputting the processed character image into the deep learning model, it is crucial to perform normalization. The normalization of the character image is to convert the representation of the image data to a range suitable for model processing, generally [0, 1] or [-1, 1]. By adjusting the scale of the data, normalization can ensure that different features have a relatively balanced impact on the model, thereby improving the convergence speed and performance of the model.

[0080] Step 8: Load the deep learning model and output the steel bar coding string.

[0081] Load the pre-trained deep learning model and pass the normalized character image as input to the model for inference and prediction. The model outputs the prediction results of each character image. By decoding these prediction results, the encoded string of the steel bar is finally obtained.

[0082] Furthermore, after obtaining the encoding string of the steel bar, since it is predicted based on the pre-trained deep learning model, further post-processing can be performed at this time to determine whether the predicted encoding string is correct and compliant, refer to step 9.

[0083] Step 9: Process the extracted encoding string of the steel bar.

[0084] Determine the number of digits and the first and last characters of the encoding string of the extracted steel bar, fill in the missing (or delete the redundant) characters for the non-compliant string according to the encoding rules, and then compare the obtained string with the steel bar code in the background database to ensure that there is no misidentification or missed identification.

[0085] Specifically, the coding string of the steel bar is generated on the steel bar by marking it with a laser marking machine based on a pre-set method. Therefore, when performing further analysis and processing, the coding string is analyzed according to the marking method and rules to determine whether the predicted coding string is compliant and correct.

[0086] Exemplarily, when the coded string obtained based on the deep learning model is post-processed, it includes: obtaining the project code characters and component package characters of the steel bar, and identifying the first character, the last character and the number of characters of the string; when it is determined that the number of characters is the standard number of characters, the string is compared with the background database, and whether it is a standard string is determined according to the comparison result; when it is determined that the number of characters is greater than the standard number of characters, the string is selected and compared according to the project code characters, the component package characters and the number of characters, and whether it is a standard string is determined according to the selection and comparison results; when it is determined that the number of characters is less than the standard number of characters, the string is supplemented according to the first character or the last character, and the supplemented string is compared with the background database to determine whether it is a standard string.

[0087] Based on the above description, it can be seen that the encoding string of the steel bar consists of several characters, such as 6, and the first one is the first character, the middle four are the middle characters, and the sixth is the last character. When the encoding string is obtained based on the deep learning model, the obtained encoding string may have certain errors due to errors in image processing or model prediction. Therefore, after obtaining the predicted encoding string, further judgment is required to determine whether it is the correct encoding string. Taking the correct number of characters of the encoding string as an example, the judgment process can refer to Figure 7 , Figure 7 It is a flowchart of post-processing of a coded string provided in an embodiment of the present application.

[0088] Reference Figure 7 In the actual processing process, for the predicted extracted character string, the number of digits of the character string is first determined, and 6 digits are the correct number of digits, and then different processing is performed according to different judgment results.

[0089] When it is determined that the number of bits of the extracted string is 6, check whether the first character and the last character in the string meet the requirements, that is, determine whether it is possible to correctly identify the construction project code and the component package identification number of the steel bar. At the same time, when identifying the coded string, it can be the identification of the same batch of steel bars, so that in the same steel bar component package, the first character and the last character are the same. After determining that the first character and the last character meet the requirements, compare the string with the steel bar code recorded in the background database to determine whether there is a match between at least 3 characters and positions. Then, when a match is determined, the recognition result is output, that is, the extracted string is output. Otherwise, a pop-up alarm and manual processing are performed. At the same time, when it is determined that the first character and the last character do not meet the requirements, a pop-up alarm and manual processing are also performed directly.

[0090] When it is determined that the number of digits of the extracted character string is greater than 6, a segment of characters ending with the project number and the component package number is extracted from the extracted character string, and the characters are compared with the steel bar code recorded in the background database to output the corresponding result.

[0091] When it is determined that the number of digits of the extracted character string is less than 6, the character string can be supplemented so that the number of digits of the supplemented character string is 6. When supplementing, the first character and the last character of the extracted character string are extracted to determine whether the first character is the project number, and whether the last character is the component package number. By supplementing the project number and / or the component package number to the extracted character string, a 6-digit character string is obtained, and then the supplemented character string is compared with the steel bar code recorded in the background database to output the corresponding result according to the comparison result.

[0092] In summary, the present application discloses a coding identification method for steel bars. In the process of identifying and processing the coding information of steel bars, when the steel bars are detected to reach the detection position, an image acquisition device is used to collect images to obtain an end face picture of the steel bars, and then the end face picture is identified and positioned to obtain a steel bar end face picture of the steel bar end face contained in the end face picture, and then the coding area in the steel bar end face picture is identified, and finally the characters contained in the coding area are identified to obtain the coding string of the steel bar. It is realized that by obtaining the end face picture of the steel bar and other information for analysis and processing, the coding information of the steel bar can be accurately and quickly identified and obtained, which effectively improves the management convenience and traceability of the steel bar material during the construction process.

[0093] According to the method described in the above embodiment, this embodiment will be further described from the perspective of the coding identification device of the steel bar. The coding identification device of the steel bar can be implemented as an independent entity or integrated in an electronic device, such as a terminal. The terminal may include a mobile phone, a tablet computer, etc.

[0094] See also Figure 8 , Figure 8 is a structural schematic diagram of a coding identification device for steel bars provided in an embodiment of the present application, such as Figure 8 As shown, the steel bar coding identification device 800 provided in the embodiment of the present application includes:

[0095] The image acquisition module 801 is used to acquire an image by using an image acquisition device to obtain an end face image of the steel bar when it is detected that the steel bar reaches the detection position;

[0096] The first processing module 802 is used to identify and locate the end face image, and obtain the steel bar end face image of the steel bar end face contained in the end face image;

[0097] The second processing module 803 is used to perform a coding area recognition process on the steel bar end face picture to obtain the coding area contained in the steel bar end face picture;

[0098] The recognition processing module 804 is used to perform character string recognition processing on the coding area to obtain the coding character string of the steel bar.

[0099] In one embodiment, the first processing module 802 is further configured to:

[0100] Perform grayscale processing on the end face image to obtain a grayscale image corresponding to the end face image;

[0101] Perform Gaussian blur processing on the grayscale image to obtain a Gaussian-processed grayscale image;

[0102] The texture feature of the grayscale image after Gaussian processing is recognized to obtain the first texture feature in the steel bar, and the steel bar end face image of the steel bar end face contained in the end face image is obtained according to the first texture feature.

[0103] In one embodiment, the first processing module 802 is further configured to:

[0104] Performing texture feature recognition processing on the steel bar end surface image to obtain a second texture feature in the steel bar end surface image;

[0105] The regional edge feature is obtained in the second texture feature according to the regional characteristics, and the coding area in the steel bar end face image is obtained based on the regional edge feature.

[0106] In one embodiment, the identification processing module 804 is further configured to:

[0107] The coding area is processed based on the projection segmentation method to obtain a projection image corresponding to the coding area;

[0108] Perform character segmentation according to the projection image to obtain the character area contained in the coding area;

[0109] Process the character area to obtain the characters corresponding to the character area;

[0110] The characters are spliced ​​together to obtain the encoding string of the steel bar.

[0111] In one embodiment, the identification processing module 804 is further configured to:

[0112] Marking the character area according to the projection image to obtain a sequential mark of the character area;

[0113] The characters are concatenated according to the sequence marks to obtain the character string corresponding to the character area;

[0114] The character string is analyzed to determine whether the character string is a standard character string, and when it is determined that the character string is a standard character string, the output character string is a coding character string of a steel bar.

[0115] In one embodiment, the steel bar coding identification device 800 further includes a judgment processing module for:

[0116] Obtain the project code character and component package character of the steel bar, and identify the first character, last character and number of characters of the character string;

[0117] When it is determined that the number of characters is the standard number of characters, the character string is compared with the backend database, and whether it is the standard character string is determined based on the comparison result;

[0118] When it is determined that the number of characters is greater than the standard number of characters, the character string is selected and compared according to the project code characters, component package characters and the number of characters, and whether it is a standard character string is determined according to the selection and comparison results;

[0119] When it is determined that the number of characters is less than the standard number of characters, the character string is supplemented according to the first character or the last character, and the supplemented character string is compared with the backend database to determine whether it is a standard character string.

[0120] In one embodiment, the steel bar coding identification device 800 further includes a marking processing module for:

[0121] When receiving a coding marking instruction, obtaining a marking character string contained in the coding marking instruction;

[0122] Control the steel bar to be marked to move to the standard position, and pre-treat the marking area of ​​the steel bar to be marked;

[0123] A character border is determined in the pre-processed area to be marked, and marking processing is performed on the character border based on the marking character string to obtain the steel bar to be marked after the marking processing.

[0124] Also, see Fig. 9 , Fig. 9 is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application, and the electronic device may be a mobile terminal such as a smart phone, a tablet computer, or the like. Fig. 9 As shown, the electronic device 900 includes a processor 901 and a memory 902. The processor 901 is electrically connected to the memory 902.

[0125] The processor 901 is the control center of the electronic device 900. It uses various interfaces and lines to connect various parts of the entire electronic device. It executes various functions of the electronic device 900 and processes data by running or loading applications stored in the memory 902 and calling data stored in the memory 902, thereby monitoring the electronic device 900 as a whole.

[0126] In this embodiment, the processor 901 in the electronic device 900 will load the instructions corresponding to the processes of one or more applications into the memory 902 according to the following steps, and the processor 901 will run the application stored in the memory 902, thereby implementing any step in the coding identification method for steel bars provided in the above embodiment.

[0127] The electronic device 900 can implement the steps in any embodiment of the coding identification method for steel bars provided in the embodiments of the present application. Therefore, it can achieve the beneficial effects that can be achieved by any coding identification method for steel bars provided in the embodiments of the present application. Please refer to the previous embodiments for details and will not be repeated here.

[0128] See also Fig.10 , Fig.10 is another structural schematic diagram of an electronic device provided in an embodiment of the present application, such as Fig.10 As shown, Fig.10 The specific structural block diagram of the electronic device provided in the embodiment of the present application is shown, and the electronic device can be used to implement the coding identification method of the steel bar provided in the above embodiment. The electronic device 1000 can be a mobile terminal such as a smart phone or a laptop computer.

[0129] The RF circuit 1010 is used to receive and send electromagnetic waves, realize the mutual conversion between electromagnetic waves and electrical signals, and thus communicate with a communication network or other devices. The RF circuit 1010 may include various existing circuit elements for performing these functions, such as antennas, radio frequency transceivers, digital signal processors, encryption / decryption chips, user identity module (SIM) cards, memories, etc. The RF circuit 1010 can communicate with various networks such as the Internet, corporate intranets, wireless networks, or communicate with other devices through wireless networks. The above-mentioned wireless networks may include cellular telephone networks, wireless local area networks, or metropolitan area networks. The above-mentioned wireless networks can use various communication standards, protocols and technologies, including but not limited to Global System for Mobile Communication (GSM), Enhanced Data GSM Environment (EDGE), Wideband Code Division Multiple Access (WCDMA), Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Wireless Fidelity (Wi-Fi) (such as Institute of Electrical and Electronics Engineers standards IEEE 802.11a, IEEE 802.11b, IEEE802.11g and / or IEEE802.11n), Voice over Internet Protocol (VoIP), Worldwide Interoperability for Microwave Access (Wi-Max), other protocols for email, instant messaging and short messages, and any other suitable communication protocols, even those that have not yet been developed.

[0130] The memory 1020 can be used to store software programs and modules, such as the program instructions / modules corresponding to the coding identification method for steel bars in the above-mentioned embodiment. The processor 1080 executes various functional applications and the coding identification method for steel bars by running the software programs and modules stored in the memory 1020.

[0131] The memory 1020 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 1020 may further include a memory remotely arranged relative to the processor 1080, and these remote memories may be connected to the electronic device 1000 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0132] The input unit 1030 can be used to receive uploaded digital or character information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, the input unit 1030 may include a touch-sensitive surface 1031 and other input devices 1032. The touch-sensitive surface 1031, also known as a touch display screen or touchpad, can collect user touch operations on or near it (such as operations performed by users using fingers, styluses, or any other suitable objects or accessories on or near the touch-sensitive surface 1031), and drive corresponding connection devices according to a pre-set program. Optionally, the touch-sensitive surface 1031 may include a touch detection device and a touch controller. Among them, the touch detection device detects the user's touch orientation, detects the signal brought by the touch operation, and transmits the signal to the touch controller; the touch controller receives the touch information from the touch detection device, converts it into touch point coordinates, and then sends it to the processor 1080, and can receive and execute commands sent by the processor 1080. In addition, the touch-sensitive surface 1031 may be implemented in various types such as resistive, capacitive, infrared, and surface acoustic wave. In addition to the touch-sensitive surface 1031, the input unit 1030 may further include other input devices 1032. Specifically, the other input devices 1032 may include, but are not limited to, one or more of a physical keyboard, a function key (such as a volume control key, a switch key, etc.), a trackball, a mouse, a joystick, and the like.

[0133] The display unit 1040 can be used to display information input by the user or information provided to the user and various graphical user interfaces of the electronic device 1000, which can be composed of graphics, text, icons, videos and any combination thereof. The display unit 1040 may include a display panel 1041, and optionally, the display panel 1041 may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. Further, the touch-sensitive surface 1031 may cover the display panel 1041, and when the touch-sensitive surface 1031 detects a touch operation on or near it, it is transmitted to the processor 1080 to determine the type of the touch event, and then the processor 1080 provides corresponding visual output on the display panel 1041 according to the type of the touch event. Although in the figure, the touch-sensitive surface 1031 and the display panel 1041 are implemented as two independent components to implement input and output functions, in some embodiments, the touch-sensitive surface 1031 and the display panel 1041 can be integrated to implement input and output functions.

[0134] The electronic device 1000 may also include at least one sensor 1050, such as a light sensor, a motion sensor, and other sensors. Specifically, the light sensor may include an ambient light sensor and a proximity sensor, wherein the ambient light sensor may adjust the brightness of the display panel 1041 according to the brightness of the ambient light, and the proximity sensor may generate an interrupt when the flip cover is closed or closed. As a type of motion sensor, the gravity acceleration sensor can detect the magnitude of acceleration in each direction (generally three axes), and can detect the magnitude and direction of gravity when stationary, which can be used for applications that identify the posture of the mobile phone (such as horizontal and vertical screen switching, related games, magnetometer posture calibration), vibration recognition related functions (such as pedometer, tapping), etc.; as for other sensors such as gyroscopes, barometers, hygrometers, thermometers, infrared sensors, etc. that can also be configured in the electronic device 1000, they will not be repeated here.

[0135] The audio circuit 1060, the speaker 1061, and the microphone 1062 can provide an audio interface between the user and the electronic device 1000. The audio circuit 1060 can transmit the received audio data to the speaker 1061 after converting the received audio data into an electrical signal, which is converted into a sound signal for output by the speaker 1061; on the other hand, the microphone 1062 converts the collected sound signal into an electrical signal, which is received by the audio circuit 1060 and converted into audio data, and then the audio data is processed by the output processor 1080 and sent to, for example, another terminal through the RF circuit 1010, or the audio data is output to the memory 1020 for further processing. The audio circuit 1060 may also include an earplug jack to provide communication between an external headset and the electronic device 1000.

[0136] The electronic device 1000 can help the user receive requests, send information, etc. through the transmission module 1070 (such as a Wi-Fi module), which provides the user with wireless broadband Internet access. Although the transmission module 1070 is shown in the figure, it is understandable that it is not a necessary component of the electronic device 1000 and can be omitted as needed without changing the essence of the invention.

[0137] The processor 1080 is the control center of the electronic device 1000. It uses various interfaces and lines to connect various parts of the entire mobile phone. By running or executing software programs and / or modules stored in the memory 1020, and calling data stored in the memory 1020, it executes various functions of the electronic device 1000 and processes data, thereby monitoring the electronic device as a whole. Optionally, the processor 1080 may include one or more processing cores; in some embodiments, the processor 1080 may integrate an application processor and a modem processor, wherein the application processor mainly processes the operating system, user interface, and application programs, and the modem processor mainly processes wireless communications. It can be understood that the above-mentioned modem processor may not be integrated into the processor 1080.

[0138] The electronic device 1000 also includes a power supply 1090 (such as a battery) for supplying power to various components. In some embodiments, the power supply can be logically connected to the processor 1080 through a power management system, so that the power management system can manage charging, discharging, and power consumption management. The power supply 1090 can also include any components such as one or more DC or AC power supplies, recharging systems, power failure detection circuits, power converters or inverters, and power status indicators.

[0139] Although not shown, the electronic device 1000 also includes a camera (such as a front camera, a rear camera), a Bluetooth module, etc., which will not be described in detail here. Specifically in this embodiment, the display unit of the electronic device is a touch screen display, and the mobile terminal also includes a memory, and one or more programs, wherein one or more programs are stored in the memory, and are configured to be executed by one or more processors to implement any step of the coding identification method of the steel bar provided in the above embodiment.

[0140] In specific implementation, the above modules can be implemented as independent entities, or can be arbitrarily combined and implemented as the same or several entities. The specific implementation of the above modules can be found in the previous method embodiments, which will not be repeated here.

[0141] Those skilled in the art can understand that all or part of the steps in the various methods of the above embodiments can be completed by instructions, or by controlling related hardware through instructions, and the instructions can be stored in a computer-readable storage medium and loaded and executed by a processor. To this end, an embodiment of the present application provides a storage medium, which stores multiple instructions, and when the instructions are executed by the processor, any step in the coding identification method of the steel bar provided in the above embodiment can be implemented.

[0142] The storage medium may include: a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.

[0143] Since the instructions stored in the storage medium can execute the steps in any embodiment of the coding identification method for steel bars provided in the embodiments of the present application, the beneficial effects that can be achieved by any coding identification method for steel bars provided in the embodiments of the present application can be achieved. Please refer to the previous embodiments for details and will not be repeated here.

[0144] The above is a detailed introduction to a coding identification method, device, electronic device and storage medium for steel bars provided in the embodiments of the present application. Specific examples are used in this article to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method and its core idea of ​​the present application. At the same time, for those skilled in the art, according to the idea of ​​the present application, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present application. Moreover, for those of ordinary skill in the art, without departing from the principles of the present application, several improvements and modifications can be made, and these improvements and modifications are also regarded as the scope of protection of the present application.

Claims

1. A coding identification method for steel bars, characterized in that: include: When it is detected that the steel bar reaches the detection position, an image acquisition device is used to acquire an image of the end face of the steel bar; Identify and locate the end face image to obtain a steel bar end face image of the steel bar end face contained in the end face image; Performing coding area recognition processing on the steel bar end face image to obtain the coding area contained in the steel bar end face image; The coding region is subjected to character string recognition processing to obtain the coding character string of the steel bar.

2. The method according to claim 1, characterized in that The step of identifying and locating the end face image to obtain a steel bar end face image of the steel bar end face contained in the end face image includes: Performing grayscale processing on the end face image to obtain a grayscale image corresponding to the end face image; Performing Gaussian blur processing on the grayscale image to obtain a grayscale image after Gaussian blur processing; The texture features of the grayscale image after Gaussian processing are identified to obtain a first texture feature in the steel bar, and a steel bar end face image of the steel bar end face contained in the end face image is obtained based on the first texture feature.

3. The method according to claim 2, characterized in that The step of performing identification processing on the coded area of ​​the steel bar end face image to obtain the coded area contained in the steel bar end face image includes: Performing texture feature recognition processing on the steel bar end surface image to obtain a second texture feature in the steel bar end surface image; According to the regional characteristics, the regional edge features are obtained in the second texture features, and the coded area in the steel bar end face image is obtained based on the regional edge features.

4. The method according to claim 1, characterized in that The step of performing character string recognition processing on the coding area to obtain the coding character string of the steel bar comprises: Processing the coding area based on a projection segmentation method to obtain a projection image corresponding to the coding area; Performing character segmentation according to the projection image to obtain a character area included in the encoding area; Processing the character area to obtain characters corresponding to the character area; The characters are spliced ​​together to obtain a coding string of the steel bar.

5. The method according to claim 4, characterized in that The splicing process is performed according to the characters to obtain the encoding string of the steel bar, including: Marking the character area according to the projection image to obtain a sequential mark of the character area; The characters are concatenated according to the sequence marks to obtain a character string corresponding to the character region; The character string is analyzed to determine whether the character string is a standard character string, and when it is determined that the character string is a standard character string, the character string is output as a coding character string of the steel bar.

6. The method according to claim 1, characterized in that After the character string recognition processing is performed on the coding area to obtain the coding character string of the steel bar, the method further includes: Obtaining the project code character and component package character of the steel bar, and identifying the first character, last character and number of characters of the character string; When it is determined that the number of characters is the standard number of characters, the character string is compared with a backend database, and whether it is the standard character string is determined according to the comparison result; When it is determined that the number of characters is greater than the standard number of characters, the character string is framed and compared according to the project code characters, the component package characters and the number of characters, and whether it is a standard character string is determined according to the framed and compared results; When it is determined that the number of characters is less than the standard number of characters, the character string is supplemented according to the first character or the last character, and the supplemented character string is compared with the backend database to determine whether it is a standard character string.

7. The method according to claim 1, characterized in that The method further comprises: When receiving a coding marking instruction, obtaining a marking character string contained in the coding marking instruction; Controlling the steel bar to be marked to move to a standard position, and pre-processing the area to be marked of the steel bar to be marked; A character frame is determined in the preprocessed area to be marked, and marking processing is performed on the character frame based on the marking character string to obtain the steel bar to be marked after the marking processing.

8. A coding identification device for steel bars, characterized in that: include: An image acquisition module is used to acquire an image by using an image acquisition device to obtain an end face image of the steel bar when it is detected that the steel bar reaches the detection position; A first processing module is used to identify and locate the end face image to obtain a steel bar end face image of the steel bar end face contained in the end face image; A second processing module is used to perform a coding area recognition process on the steel bar end face image to obtain the coding area contained in the steel bar end face image; The recognition processing module is used to perform character string recognition processing on the coding area to obtain the coding character string of the steel bar.

9. An electronic device, characterized in that: The electronic device comprises a processor, a memory, and a computer program stored in the memory and executable on the processor, and the processor implements the steps in the method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

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