TWI625174B - Method for detecting fracture of steel strip tail - Google Patents

Abstract

A method for detecting damage at the end of a steel strip comprises: extracting a plurality of statistical characteristic descriptors corresponding to each of a normal state image of the tail end of the steel strip and a damaged state image of the tail end of the steel strip; and filtering from the statistical characteristic descriptor A plurality of discriminative statistical characteristic descriptors are generated; a prediction model is established from a statistical characteristic description of the discriminative force corresponding to each of the normal state image of the tail end of the steel strip and the image of the broken end state of the steel strip; The steel strip is subjected to the tail end image judging step to obtain an image of the unknown state at the end of the steel strip; the statistical characteristic descriptor of the discrimination force corresponding to the image of the unknown state at the end of the steel strip is taken; and the prediction model is used to predict whether the steel strip is used There is a broken end.

Description

Method for detecting damage at the end of steel strip

The disclosure relates to a method for detecting the damage of the tail end of a steel strip, and in particular to a method for detecting the damage of the tail end of an image-based steel strip.

In the current steel strip production process, after the steel embryo is heated by the heating furnace, the rough rolling and finishing rolling steps are carried out to obtain a steel strip having a desired thickness. However, in the rolling process of the production line, the steel strip may have a side walk, which may cause abnormal defects such as breakage of the steel strip tail. When the steel strip enters the rolling implement, the damage of the tail end of the steel strip will impact the roll of the rolling implement, and the surface of the roll will be uneven. This uneven defect causes the roll to transfer the defect on the surface of the roll to the steel strip when the next steel strip is rolled, which will cause the strip of the strip which is subsequently rolled by the roll to form a defect and be rejected. This abnormality in the transfer of the surface defects of the rolls due to the abnormal rolling at the end of the steel strip is called Tail Pinch. In order to solve the above problem, a method is needed to detect whether the end of the steel strip is broken.

The purpose of the disclosure is to provide an image-based detection method for the damage of the tail end of a steel strip, and firstly analyze the normal state image of the tail end of the steel strip and the image of the broken end state of the steel strip to establish a prediction model. Then, the steel strip in progress is imaged, and after the image of the tail end of the steel strip is obtained, the previously established prediction model is used to predict whether the tail end of the steel strip is broken.

According to the above object of the present disclosure, a method for detecting the damage of the tail end of a steel strip is provided, which comprises: obtaining a normal state image of a plurality of steel strip tail ends and an image of a broken state of a plurality of steel strip tail ends; performing an image processing step to extract steel A plurality of statistical property descriptors corresponding to each of the image of the normal state of the tail end and the image of the broken end state of the steel strip; performing a screening step to screen a plurality of discriminative statistics from the statistical property descriptors Characterization descriptor; performing a modeling step to establish a prediction model from the statistical characteristic description of the discriminative force corresponding to each of the normal state image of the steel strip tail end and the broken end state image of the steel strip; Carrying a tail image judgment step to obtain an image of an unknown state at the end of the steel strip; performing an image processing step to extract a statistical characteristic descriptor corresponding to the discriminative force of the image of the unknown end of the steel strip; and performing a prediction step to utilize the prediction The model is used to predict whether the steel strip is damaged at the end.

In some embodiments, each of the normal state image of the tail end of the steel strip, the image of the broken end state of the steel strip, and the image of the unknown state of the tail end of the steel strip are gray scale images.

In some embodiments, the image processing step includes: converting a grayscale image into a binarized image to distinguish a foreground and a background by using a preset grayscale threshold; using the foreground to determine a plurality of boundary contour points; boundary The contour points are subjected to an analysis operation to find a statistical property descriptor.

In some embodiments, the image processing step further includes: dividing the grayscale image into a plurality of blocks; and performing grayscale statistics on each of the blocks to determine a block average grayscale and block of each block. Gray standard deviation, where the block average gray level and the block gray level standard deviation are two of the statistical characteristic descriptors.

In some embodiments, the screening step includes: performing a variance analysis on the statistical property descriptor; and screening the statistical property descriptor with the discriminative power using a preset confidence level.

In some embodiments, the modeling step includes: normalizing the statistical characteristic descriptor with discriminative power; performing binary encoding on the discriminative statistical characteristic descriptor that has been normalized; and performing linear regression operation To calculate the relationship function.

In some embodiments, the tail image determining step includes the following steps: (S5a) taking an image of the traveling steel strip to obtain a steel strip image; (S5b) using the steel strip image to define a region of interest (region of Interest, ROI); (S5c) comparing the average gray scale value change of the region of interest to determine whether the steel strip image corresponds to the tail end of the steel strip; if the judgment result is negative, repeat steps (S5a) to (S5c); If the result of the determination is YES, the step (S5d) is performed; and (S5d) the image of the steel strip is taken as an image of an unknown state at the end of the steel strip.

In some embodiments, the steel strip travels along a first direction, and the region of interest has a first side and a second side opposite the first direction, wherein the comparing the average grayscale value changes comprises the following steps: The gray level value of one side in the second direction perpendicular to the first direction is the first flat a gray scale value; calculating a gray scale value of the second side in the second direction to be a second average gray scale value; and when the difference between the first average gray scale value and the second average gray scale value is greater than a preset gray scale The difference threshold indicates that the judgment result of the step (S5c) is YES.

In some embodiments, the predicting step comprises: normalizing the statistical characteristic descriptor of the discriminative force corresponding to the image of the unknown state at the end of the steel strip; and using the relational function to discriminate the normalized processing The statistical property descriptor performs an operation to obtain a predicted value; and if the predicted value is greater than a preset threshold value, it represents that the steel strip has a tail end breakage.

In some embodiments, a warning is issued when the steel belt is damaged at the end.

The above described features and advantages of the present invention will be more apparent from the following description.

1000‧‧‧Detection method

S1-S7, S2a-S2c, S5a-S5d‧‧‧ steps

101, 102‧‧‧ Areas of interest

101A, 102A‧‧‧ first side

101B, 102B‧‧‧ second side

A _F , A _C , a _C ‧ ‧ area

P _F , P _C ‧‧‧ total length

(X _F , Y _F ), (u _F , v _F ), (U _F , V _F ), (x _F , y _F ) ‧ ‧ punctuation

θ _p ‧‧‧ foreground image positive angle

θ _n ‧‧ ‧ foreground image negative angle

L _i,1 ,L _i,2 ‧‧‧The length of the corner of the foreground image

A better understanding of the aspects of the present disclosure can be obtained from the following detailed description taken in conjunction with the drawings. It should be noted that, according to industry standard practices, the features are not drawn to scale. In fact, in order to make the discussion clearer, the dimensions of each feature can be arbitrarily increased or decreased.

FIG. 1 is a flow chart showing a method for detecting a broken end of a steel strip according to an embodiment of the present disclosure.

FIG. 2 is a flow chart showing steps of image processing according to an embodiment of the present disclosure.

[Fig. 3a] - [Fig. 3c] are diagrams for illustrating a plurality of statistical characteristic descriptors for describing image appearance characteristics according to an embodiment of the present disclosure.

[Fig. 3d] is a schematic diagram for explaining a plurality of statistical characteristic descriptors for describing grayscale characteristics of an image according to an embodiment of the present disclosure.

FIG. 4 is a flow chart showing a tail end image determining step according to an embodiment of the present disclosure.

[Fig. 5] An exemplary embodiment is shown to illustrate the comparison of the average grayscale value variation of the region of interest.

Embodiments of the invention are discussed in detail below. However, it will be appreciated that the embodiments provide many applicable concepts that can be implemented in a wide variety of specific content. The examples discussed and disclosed are illustrative only and are not intended to limit the scope of the invention. The terms "first", "second", "etc." used in this document are not intended to mean the order or the order, and are merely to distinguish between elements or operations described in the same technical terms.

In the present disclosure, an image capturing device is disposed between a plurality of rolling mill stations of the steel strip, the image capturing device picks up the steel strip image from above the steel strip, and the image capturing device is electrically connected to the computer device to utilize the computer device Image processing was performed to analyze the image of the steel strip between each rolling station.

FIG. 1 is a flow chart showing a method 1000 for detecting a broken end of a steel strip according to an embodiment of the present disclosure. In step S1, a normal state image of the tail end of the plurality of steel strips and a broken state image of the end of the plurality of steel strips are obtained. It should be noted that the image of the steel strip obtained in step S1 is a gray scale image and is a tail end of the steel strip. The state of the steel strip is known (the tail end of the steel strip is normal or the tail end of the steel strip is broken), so the difference between the normal state image of the tail end of the steel strip and the damage state of the tail end of the steel strip can be obtained in the subsequent steps. To analyze.

In step S2, an image processing step is performed to extract a plurality of statistical property descriptors corresponding to each of the normal state image of the steel strip tail end and the steel strip tail end damage state image. FIG. 2 is a flow chart showing an image processing step S2 according to an embodiment of the present disclosure. First, in step S2a, the grayscale image obtained in step S1 is converted into a binarized image by using a preset grayscale threshold to distinguish the foreground from the background. In the present disclosure, the image conversion method is to first perform histogram statistics on the grayscale image, and then calculate a preset grayscale threshold value, wherein the calculation method of the preset grayscale threshold value may be using the Ou Su method (Otsu Method), Maximum entropy method or K-means method. Then, the pixels in the grayscale image whose image grayscale value exceeds the preset grayscale threshold are set as the foreground, and vice versa, as the background, thereby converting the grayscale image into the binarized image.

In step S2b, a plurality of boundary contour points are obtained using the foreground, wherein the boundary contour points can be used to depict the foreground image contour. Next, in step S2c, the boundary contour points are subjected to an analysis operation to obtain a plurality of statistical property descriptors, wherein the statistical property descriptors may be used to describe image appearance characteristics or to describe image grayscale characteristics. Table 1 lists a number of statistical characterization descriptors for describing image appearance characteristics of the present disclosure. 3a to 3c are diagrams for explaining a plurality of statistical characteristic descriptors for describing an image appearance characteristic according to an embodiment of the present disclosure.

Referring to Figure 3a, wherein the duplex portion is the foreground image, in the embodiment of the present invention, in order to represent the foreground image A _F area to (X _F, Y _F) to represent the foreground image coordinates of the center, to be P _F Indicates the foreground image boundary length (total length of the boundary), and (u _F , v _F ) represents the nearest point coordinate of the foreground image boundary from the foreground image center, and (U _F , V _F ) indicates that the foreground image boundary is farthest from the foreground image center. Point coordinates. Therefore, the foreground image boundary irregularity is equivalent to

Referring to FIG. 3b, the dotted line portion is a convex hull image, and the convex hull image is defined as a convex polygon that can include the smallest area of all the point sets of the foreground image. In the embodiment of the present invention, the convex envelope image area is represented by A _C , the convex envelope image center coordinates are represented by (x _F , y _F ), and the convex envelope image boundary length (total length of the boundary) is represented by P _C . Therefore, the ratio of the area difference between the foreground image and the convex hull image is equivalent to The ratio of the difference between the foreground image and the convex envelope image boundary length is equivalent to , foreground image compactness (compactness) is equivalent The foreground image and the convex hull image difference image area area a _{C is} equivalent to A _C -A _F , and the foreground image and the convex hull image difference image area center Y coordinate are equivalent to y _F -Y _F .

Referring to FIG. 3c, the foreground image has a plurality of foreground images, wherein the corners indicate a positive angle of the foreground image by θ _p , a negative corner of the foreground image by θ _{n , and} a foreground image bend by L _i,1 , L _i,2 The number of pixels whose angle overlaps the foreground image outline. It should be noted that in the present embodiment, the foreground image positive corner θ _p and the foreground image negative corner θ _n are positive angles, that is, the counterclockwise direction is a positive direction in which the angle is increased. The positive angle θ _p of the foreground image is defined as the angle of the foreground image where the angle is greater than 45 degrees and L _i,1 and L _{i,2 are} greater than 3 pixels, and the negative angle θ _n of the foreground image is defined as the angle less than -45 degrees and L _{i, 1} and L _{i, 2} are foreground image angles greater than 3 pixels. It is worth mentioning that, in order to simplify the indication, FIG. 3c only shows a foreground image positive corner θ _p and a foreground image negative corner θ _n . In fact, the foreground image may have more corners. In the embodiment of the present invention, N _{p is} used to indicate the number of positive corners of the foreground image boundary, N _{n is} used to represent the number of negative corners of the foreground image boundary, and θ _{max is} used to represent the maximum positive corner angle of the foreground image boundary. θ _{min is} used to represent the minimum negative corner angle of the foreground image boundary, To represent the average positive corner angle of the foreground image boundary, To represent the average negative corner angle of the foreground image boundary. Therefore, the maximum positive corner bending energy of the foreground image boundary is equivalent to , the foreground image boundary minimum negative bending angle bending energy is equivalent , the foreground image boundary average positive bending angle bending energy is equivalent , the foreground image boundary average negative bending angle bending energy is equivalent

Table 2 lists a number of statistical feature descriptors used to describe the grayscale characteristics of the image. FIG. 3d is a schematic diagram for explaining a plurality of statistical characteristic descriptors for describing grayscale characteristics of an image according to an embodiment of the present disclosure.

Please refer to FIG. 3d, in which the gray bottom portion is a gray scale image. The average gray level μ _T of the original image is equivalent to the original gray level of the original pixel within the height of the top 1/5 of the gray image contour. The average gray level μ _B of the original image is equivalent to the gray image contour line at the bottom 1 The original pixel average grayscale in the /5 height range, the original image center average grayscale μ _{C is} equivalent to the grayscale image outline excluding the top grayscale of the original pixel outside the top 1/5 and bottom 1/5 height range. Therefore, the difference between the top/bottom of the original image and the center average gray scale is equivalent to max (μ _B -μ _C , μ _T -μ _C ).

In this embodiment, the image processing step S2 further includes: dividing the grayscale image into a plurality of blocks; and performing grayscale statistics on each of the blocks to determine the average grayscale and the block of each block. Gray standard deviation, where the block average gray level and the block gray level standard deviation are two of the statistical characteristic descriptors. Referring to FIG. 3d and Table 2 again, in the present embodiment, the grayscale image is divided into 3×3 blocks, but the present invention is not limited thereto. Then, gray scale statistics are performed on each block to find the original image block average gray level μ _j of each block and the original image block gray level standard deviation σ _j .

Returning to FIG. 1, in step S3, a screening step is performed to filter a plurality of discriminative statistical property descriptors from the statistical property descriptors. In the present disclosure, an analysis of variance (ANOVA) method is used to calculate whether each statistical property descriptor is significant. In the present disclosure, there are two types of (group) data for each statistical property descriptor, that is, the normal end of the steel strip and the tail end of the steel strip are broken, and two types of statistical characteristic descriptors are respectively calculated (group). The mean-group variability and the average-group variability of the data, the F-value ((mean-group variation)/(mean-group variation) in the variance analysis method ). Then define a confidence level, for example 95%. If the F value of a statistical property descriptor exceeds the confidence level defined above, it indicates that the statistical property descriptor is significant, that is, the statistical property description The child is a statistical feature descriptor with discriminative power.

Referring again to FIG. 1, in step S4, a modeling step is performed to establish a prediction model from a statistical description of the discriminative statistical characteristics corresponding to each of the normal state image of the steel strip tail end and the broken end state image of the steel strip end. . In the above modeling step, each of the discriminative statistical characteristic descriptors is first normalized, wherein the purpose of the normalization processing is to adjust the scale of the discriminative statistical characteristic descriptor to one Reasonable For example, the scale ranges from 0 to 1 or -1 to +1, which facilitates subsequent operations and ensures subsequent modeling quality. In the present disclosure, the scale adjustment of the normalization process may be a scaling or zero-mean normalization (ie, subtracting the average and dividing by the standard deviation). In the above modeling step, the discriminative statistical property descriptors that have been normalized are then binary coded and linear regression operations are performed to calculate the relationship function.

The following is an example to illustrate how to use a linear regression operation to calculate a relational function, wherein the number of statistical characteristic descriptors having discriminative power is n, and in step S1, the number of normal state images at the end of the steel strip For i sheets, the number of broken state images of the end of the steel strip is j sheets, and i+j=m. The value of the first statistical characteristic descriptor of the first discriminating state of the first strip of steel strip is a _1,1 , and the nth discriminative statistical characteristic of the normal state image of the end of the i-th strip The value of the descriptor is a _i,n , and the value of the first discriminative statistical characteristic descriptor of the image of the broken end of the first steel strip is a _i+1,1 , and the j-th steel strip end is broken. The value of the nth discriminative statistical property descriptor of the state image is a _m,n . Therefore, the matrix A _m×n can be used to represent the statistical characteristic descriptor with discriminative power, and the matrix B _m×1 can be used for binary encoding, where 0 represents the normal end of the steel strip, and 1 represents the broken end of the steel strip, then the matrix Among the B _m×1 , there are a total of 0, and 1 has a total of j. The matrix A _{m×n is expressed by the} following formula (1), and the matrix B _{m×1 is} as shown in the following formula (2):

Through the linear regression operation, the relationship between the matrix A _{m × n} and the matrix B _{m × 1} is A _{m × n} * X _{n × 1} = B _{m × 1} , so the operation of the relational function X _{n × 1} is calculated. The formula is X _{n × 1} = (A ^T A) ^-1 (A ^T B).

It should be noted that the modeling step in step S4 of the present disclosure does not limit the use of linear regression methods for modeling, and the modeling method may also utilize an artificial neural network or a support vector machine (support). Vector machine) and other regression methods.

Referring to FIG. 1 again, in step S5, the trailing end image judging step is performed on the traveling steel strip to obtain an image of an unknown state at the end of the steel strip. FIG. 4 is a flow chart showing a tail end image determining step S5 according to an embodiment of the present disclosure. In step S5a, the traveling steel strip is imaged to obtain a steel strip image. In step S5b, the steel strip image is used to define a region of interest (ROI). Since the width of the steel strip may not be consistent in the steel strip image, the height of the region of interest must be the width of the steel strip. The narrowest portion can be included, and the length of the region of interest is equivalent to the total length that the image capturing device can take in the direction of travel of the steel strip. In step S5c, an average grayscale value change comparison is performed on the region of interest to determine whether the steel strip image corresponds to the trailing end of the steel strip.

The following describes an example of how step S5c is performed. FIG. 5 illustrates an exemplary example to illustrate the average grayscale value change of the region of interest. Comparison. It should be noted that since the rolled steel strip exhibits a red hot state, the steel strip representing the steel strip, that is, the gray block in Fig. 5, has a higher gray scale value, and vice versa, in the steel strip image. It is not representative of the steel strip, it is essentially dark (black), so it has a lower gray scale value. As shown in FIG. 5, the regions of interest 101, 102 have opposite first sides 101A, 102A and second sides 101B, 102B in the direction of travel of the steel strip, and the first sides 101A, 102A of the regions of interest 101, 102 are calculated. The gray scale value of each pixel point perpendicular to the traveling direction of the steel strip is averaged as a first average gray scale value, and the second sides 101B, 102B of the regions of interest 101, 102 are calculated to be perpendicular to the traveling direction of the steel strip. The grayscale value of the pixel is on average the second average grayscale value. When the steel strip image is the tail end image of the steel strip, as shown in the left figure of FIG. 5, the first side 101A and the second side 101B representing the region of interest 101 have a significant gray scale value difference, so the preset gray scale can be defined. The difference threshold, for example, 100 gray scale, when the difference between the first average gray scale value and the second average gray scale value is greater than the preset gray scale difference threshold, the steel strip image is the end image of the steel strip. When the steel strip image is not the end image of the steel strip, as shown in the right figure of FIG. 5, the first side 102A and the second side 102B representing the region of interest 102 do not have a significant gray scale value drop, that is, when the first The difference between the average grayscale value and the second average grayscale value is not greater than the preset grayscale difference threshold, and the steel strip image is not the end image of the steel strip. It is worth mentioning that, as shown in the left figure of FIG. 5, when the first side 101A and the second side 101B of the region of interest 101 have a significant gray scale value difference, and the gray scale of the first side 101A of the region of interest 101 The value is lower than the second side 101B of the region of interest 101, and the steel strip image is the end of the steel strip. Conversely, if the first side and the second side of the region of interest have significant grayscale value differences, and the grayscale value of the first side of the region of interest is higher than the region of interest On the two sides, the image of the steel strip is the end of the steel strip. In the step S5c of the present disclosure, it is determined whether the steel strip image corresponds to the tail end of the steel strip.

Referring back to FIG. 4, if it is determined in step S5c that the steel strip image is not the tail end image of the steel strip, step S5a to step S5c are repeated, that is, the taking of the steel strip in progress is continued until the judgment of step S5c is performed. The result is yes. When the result of the determination in the step S5c is YES, the process proceeds to a step S5d, and the image of the steel strip is taken as an image of the unknown state of the end of the steel strip.

Returning to FIG. 1, in step S6, an image processing step is performed to extract a statistical characteristic descriptor of the discrimination force corresponding to the image of the unknown state at the end of the steel strip. The step S6 is substantially the same as the step S2. The only difference is that the step S6 is for the unknown state image of the tail end of the steel strip and only the statistical characteristic descriptor having the discriminating power is extracted, so it will not be described here.

Referring to FIG. 1, in step S7, a prediction step is performed to predict whether the steel strip is damaged at the end end by using a prediction model. In the above-mentioned prediction step, the statistical characteristic descriptor of the discrimination force corresponding to the unknown state image of the tail end of the steel strip is first normalized, and the normalization processing has been described in the description of the above step S4, and Let me repeat. In the above-described prediction step, the relational function of the discriminative statistical characteristic that has been subjected to the normalization processing is then operated by the relational function to obtain the predicted value.

The following is an example to illustrate how to use the relational function to obtain the predicted value. The number of statistical characteristic descriptors with discriminative power is also n, and the first one of the unknown state image of the steel strip has the discriminative power. The value of the statistical characteristic descriptor is c ₁ , and the value of the nth discriminative statistical characteristic descriptor of the unknown state image of the steel strip end is c _n , so the matrix C _1×n can be used to represent the end of the steel strip. The statistical characteristic descriptor of the discriminative force corresponding to the unknown state image, that is, the matrix C _{1 × n} = (c ₁ ... c _n ). Next, the predicted value b is obtained by using the relation b = C _{1 × n} * X _{n × 1} .

In the above prediction step, finally, the preset threshold value is defined, for example, the preset threshold value is 0.5, and if the predicted value is greater than the preset threshold value, the steel strip is damaged at the end. In addition, when the steel belt is damaged at the end, the warning device can be used to send a warning to notify the site personnel that the steel belt is damaged at the end, so that the field personnel can take appropriate measures to avoid the steel. Abnormality in the transfer of the surface defects of the roll due to breakage at the end.

It can be seen from the above that the method for detecting the damage of the tail end of the steel strip of the present invention first analyzes the normal state image of the tail end of the steel strip and the image of the broken end state of the steel strip to establish a prediction model, and then for the steel in progress. The belt is taken for imaging, and after the image of the tail end of the steel strip is obtained, the previously established prediction model is used to predict whether the tail end of the steel strip is broken.

The features of several embodiments are summarized above, and those skilled in the art will be able to understand the aspects of the disclosure. Those skilled in the art will appreciate that the present disclosure can be readily utilized as a basis for designing or modifying other processes and structures, thereby achieving the same objectives and/or achieving the same advantages as the embodiments described herein. . It should be understood by those skilled in the art that the invention may be made without departing from the spirit and scope of the disclosure.

Claims (8)

A method for detecting damage at the end of a steel strip comprises: obtaining a normal state image of the end of the plurality of steel strips and an image of the broken end state of the plurality of strips; performing an image processing step to extract the normal state of the tail ends of the strips a plurality of statistical characterization descriptors corresponding to each of the image of the broken end state of the steel strip; performing a screening step to screen a plurality of discriminative statistics from the statistical characterization descriptors Characterization descriptor; performing a modeling step to establish a statistical characteristic description from the normal state image of the tail end of the steel strip and the discriminative statistical characteristic image corresponding to each of the tailband damage state images of the steel strips Predicting a model; performing a tail end image determining step on a traveling steel strip to obtain an image of an unknown state at the end of the steel strip; performing the image processing step to capture the corresponding image of the unknown end image of the steel strip tail end a statistical characteristic descriptor of the discriminating power; and performing a predicting step to predict whether the steel strip has a tail end breakage; wherein the steel strip tails are in a normal state The image processing step includes: using a preset grayscale threshold to use the grayscale Convert image into one Binarizing the image to distinguish a foreground from a background; using the foreground to find a plurality of boundary contour points; and performing an analysis operation on the boundary contour points to obtain the statistical property descriptors. The method for detecting the damage of the tail end of the steel strip as described in claim 1, wherein the image processing step further comprises: dividing the gray scale image into a plurality of blocks; and performing graying on each of the blocks The order statistics are used to find the average gray level of one block of each of the blocks and the gray standard deviation of one block; wherein the average gray level of the block and the standard deviation of the gray level of the block are the statistical characteristic descriptors. Two of them. The method for detecting the damage of the tail end of the steel strip according to the first aspect of the patent application, wherein the screening step comprises: performing a variance analysis on the statistical characteristic descriptors; and filtering out the predetermined confidence level. Some discriminative statistical characterization descriptors. The method for detecting the damage of the tail end of the steel strip as described in claim 1, wherein the modeling step comprises: performing a normalization process on the discriminative statistical characteristic descriptors; Discriminating statistical properties The descriptor is binary coded; and a linear regression operation is performed to calculate a relational function. The method for detecting the damage of the tail end of the steel strip according to claim 1, wherein the tail end image determining step comprises the following steps: (S5a) taking the image of the steel strip in progress to obtain a steel strip image (S5b) using the steel strip image to define a region of interest (ROI); (S5c) performing an average grayscale value change comparison on the region of interest to determine whether the strip image corresponds to The tail end of the steel strip; if the judgment result is negative, repeat steps (S5a) to (S5c); if the judgment result is yes, proceed to step (S5d); and (S5d) take the steel strip image as the end of the steel strip Unknown state image. The method for detecting a broken end of a steel strip according to claim 5, wherein the steel strip travels along a first direction, and the region of interest has a first side opposite to the first side. And comparing with the second side, wherein the average grayscale value change comprises: calculating, by the first side, a grayscale value in a second direction perpendicular to the first direction to average a first average grayscale value; Calculating, by the second side, the grayscale value in the second direction is a second average grayscale value; and when the difference between the first average grayscale value and the second average grayscale value is greater In a preset grayscale difference threshold, the judgment result of the step (S5c) is YES. The method for detecting the damage of the tail end of the steel strip as described in claim 4, wherein the predicting step comprises: performing the discriminative statistical characteristic descriptor corresponding to the image of the unknown state at the end of the steel strip Normalizing processing; using the relational function to calculate the discriminative statistical property descriptors that have undergone the normalization process to obtain a predicted value; and if the predicted value is greater than a predetermined threshold value, Represents the steel belt with a broken end. For example, in the method for detecting the damage of the tail end of the steel strip according to the first aspect of the patent application, when the steel strip has a broken end, a warning is issued.