TW202412960A - System and method for detecting and compensating depth - Google Patents

Abstract

A system includes an object to be tested, multiple rotation modules, a depth sensing module and a computing module. The rotation modules rotate the object around an axis. The depth sensing module is arranged on one side of the object to obtain a rectangular depth image having a sampling direction and a rotation direction. The computing module applies a sliding window on the rectangular depth image, and the sliding window moves along the sampling direction to obtain multiple depth values. The computing module determines whether there is a deviation in the rectangular depth image according to the depth values, and compensates the deviation if so.

Description

Depth detection and compensation system and method

The present disclosure relates to a detection system and method for rotating an object to be tested and scanning the side depth of the object to be tested, and in particular, can compensate for the deviation caused by the rotation.

In steel mills, the thickness of steel strips after cold rolling is relatively thin, which is prone to edge cracks or sawtooth defects. In order to avoid problems caused by steel strips with edge defects in subsequent processes, it is necessary to use a detection system to check whether the edges of the steel strips are defective. One method is to use a camera to shoot the side of the steel coil after winding the steel strip into a coil, but because the radius of the steel coil is quite large, it is difficult for the camera to cover the entire range of the steel coil.

The disclosed embodiment proposes a depth detection and compensation system, including an object to be measured, multiple rotation modules, a depth sensing module and a calculation module. The rotation module is used to rotate the object to be measured around an axis, and the outline of the object to be measured viewed from the axis is circular. The depth sensing module is arranged on one side of the object to be measured to obtain a rectangular depth image, wherein the rectangular depth image has a sampling direction and a rotation direction, and the scanning range of the depth sensing module is smaller than the diameter of the circle. The calculation module is communicatively connected to the rotation module and the depth sensing module, and is used to apply a sliding window to the rectangular depth image, and the sliding window moves along the sampling direction to obtain multiple depth values. The calculation module is used to determine whether the rectangular depth image has deviation according to the depth value, and if so, compensate for the deviation.

In some embodiments, the length of the sliding window in the sampling direction is greater than 1, and the length of the sliding window in the rotation direction is equal to the length of the rectangular depth image in the rotation direction. The calculation module is used to execute a moving average algorithm according to the sliding window, and each position of the sliding window corresponds to a depth value.

In some embodiments, the calculation module is further used to calculate the slope between every two adjacent depth values, and determine whether the rectangular depth image has a linear deviation or a non-linear deviation based on whether the slope exceeds a range.

In some embodiments, the calculation module is further used to execute a linear regression algorithm according to the depth value, and determine whether the rectangular depth image has a linear deviation or a non-linear deviation according to the result of the linear regression algorithm.

In some embodiments, the object to be tested is a cylindrical steel coil.

From another perspective, the embodiment of the present disclosure proposes a depth detection and compensation method applicable to a computer system. The depth detection and compensation method includes: rotating the object to be detected around an axis through multiple rotation modules, wherein the outline of the object to be detected viewed from the axis is a circle; obtaining a rectangular depth image through a depth sensing module, wherein the depth sensing module is set on one side of the object to be detected, the rectangular depth image has a sampling direction and a rotation direction, and the scanning range of the depth sensing module is smaller than the diameter of the circle; applying a sliding window on the rectangular depth image, the sliding window moves along the sampling direction to obtain multiple depth values; and judging whether the rectangular depth image has deviation according to the depth value, and if so, compensating for the deviation.

In some embodiments, the length of the sliding window in the sampling direction is greater than 1, and the length of the sliding window in the rotation direction is equal to the length of the rectangular depth image in the rotation direction. The depth detection and compensation method also includes: executing a moving average algorithm based on the sliding window, and each position of the sliding window corresponds to one of the depth values.

In some embodiments, the depth detection and compensation method further includes: calculating the slope between two adjacent depth values, and determining whether the rectangular depth image has a linear deviation or a non-linear deviation based on whether the slope exceeds a range.

In some embodiments, the depth detection and compensation method further includes: executing a linear regression algorithm according to the depth value, and determining whether the rectangular depth image has a linear deviation or a non-linear deviation according to the result of the linear regression algorithm.

In order to make the above features and advantages of the present invention more clearly understood, embodiments are specifically cited below and described in detail with reference to the accompanying drawings.

The terms “first,” “second,” etc. used herein do not particularly refer to order or sequence, but are only used to distinguish elements or operations described with the same technical term.

FIG1 is a schematic diagram of a detection system according to an embodiment. Referring to FIG1 , the depth detection and compensation system 100 includes an object to be detected 110, a plurality of rotation modules 121-122, a depth sensing module 130, and a calculation module 140. The object to be detected 110 is, for example, a cylindrical steel coil, but the present disclosure is not limited thereto. In other embodiments, the object to be detected 110 may also be cylindrical or disc-shaped, and the material of the object to be detected 110 may include any metal, organic matter, metal compound, and the like. The rotation modules 121-122 may include rollers and motors for rotating the object to be detected 110 around an axis 150.

The depth sensing module 130 may include a structured light module, an infrared module, a dual camera module, a laser distance sensor, or any module that can sense the depth of a scene. The object 110 to be tested has two sides on the axis 150. When viewed from any side, the outline (the outermost shape) of the object 110 to be tested is a circle. The depth sensing module 130 is disposed on one of the sides to photograph the side of the object 110 to be tested. In some embodiments, the depth sensing module 130 is a line scanner. When the object 110 to be tested rotates, the depth sensing module 130 continues to scan. The captured image is called a rectangular depth image. In particular, the scanning range of the depth sensing module 130 is smaller than the diameter of the circle, for example, only a radius greater than or equal to the circle is required.

The computing module 140 is communicatively connected to the depth sensing module 130 and the rotation modules 121-122. The communication connection can be achieved by any wired or wireless communication means. The computing module 140 is, for example, a central controller, a processor, a computer system, a server, or any electronic device with computing capabilities. The computing module 140 controls the rotation modules 121-122 to determine the angular velocity of the rotation of the object to be measured 110. The computing module 140 also receives a rectangular depth image from the depth sensing module 130.

The object to be measured 110 may have some displacement when rotating, and these displacements will cause deviations in the sensed rectangular depth image. For example, axis 160 is perpendicular to axis 150, and the object to be measured 110 may rotate along axis 160 at an angle, causing a portion of the object to be measured 110 to be closer to the depth sensing module 130 and another portion to be farther from the depth sensing module 130, which causes the sensed rectangular depth image to have a linear deviation. In addition, the rotation speed of the object to be measured 110 may be inconsistent, and such an irregular rotation speed will cause the sensed rectangular depth image to have a non-linear deviation. The calculation module 140 will execute a depth detection and compensation method to detect whether the rectangular depth image has deviations, and if so, will compensate for the deviations. This method will be described below.

FIG2 is a flow chart of a depth detection and compensation method according to an embodiment. FIG3 is a schematic diagram of a detection deviation according to an embodiment. Referring to FIG2 and FIG3, in step 201, a rectangular depth image 300 is obtained through a depth sensing module, and the rectangular depth image 300 has a sampling direction 301 and a rotation direction 302. The sampling direction 301 is also the scanning direction of the line scanner, and the rotation direction 302 is the rotation direction of the object to be measured 110. The line scanner can generate a row of pixels each time it scans, and the gray level of each pixel represents the depth. As the object to be measured 110 rotates, the line scanner will obtain the next row of pixels at the next sampling time.

In step 202, it is determined whether the rectangular depth image 300 has a deviation. If there is a deviation, the type of deviation is further determined in step 203. Specifically, a sliding window 310 may be applied to the rectangular depth image 300. The length of the sliding window 310 in the sampling direction 301 is greater than 1 (e.g., 3), and the length of the sliding window 310 in the rotation direction 302 is, for example, equal to the length of the rectangular depth image 300 in the rotation direction 302. The sliding window 310 is used to execute a moving average algorithm, that is, to calculate the average of all pixels in the sliding window 310. The sliding window 310 moves along the sampling direction 301, for example, by one or more pixels each time, and an average value can be calculated for each position during the movement. In some embodiments, the ranges of the sliding windows 310 at different positions may overlap each other.

For the purpose of explanation, the average calculated by the moving average algorithm is referred to as the depth value below. Next, whether the rectangular depth image 300 has deviation is determined based on these depth values. In an ideal situation without deviation, the distribution of the depth value is similar to that shown in the graph 320, wherein the horizontal axis is the sampling direction and the vertical axis is the magnitude of the depth value. Each point 321 in the graph 320 represents a depth value. For the sake of simplicity, not all the depth values 321 are marked here. In addition, the straight line 322 represents the trend of these depth values 321. In an ideal situation, the slope of the straight line 322 should be close to 0.

As described above, when the object to be tested 110 rotates an angle along the vertical axis 160, there will be a linear deviation. At this time, the distribution of the depth values will be similar to the graph 330. These depth values 331 will gradually decrease, so the slope of the straight line 332 representing the trend will be less than 0; or the depth value 331 will gradually increase, and the slope of the straight line representing the trend will be greater than 0. When there is a non-linear deviation, a graph 340 will be formed, and the depth value 341 will also gradually decrease, but the decreasing speed in different segments 341~344 is different, that is, the slope of segment 342 will be greater than the slope of segment 343, and the slope of segment 343 will be greater than the slope of segment 344. Alternatively, the depth value 341 may gradually increase, but the slope in different segments is different. Here, the slope or linear regression algorithm can be used to determine which type of deviation it belongs to.

First, the slope calculation method is explained. First, the slope between every two adjacent depth values is calculated from left to right (along the sampling direction 301). represents the i-th slope, i is a positive integer. Next, a range is set, expressed as -R~+R, where R is a positive integer. In addition, a reference value T is also set, and its initial value is set to 0. For each depth value ,if If true, a new fragment is generated and a new baseline value is set , otherwise, process the next depth value. After processing all the depth values, determine the number of segments. If the number of segments is 0, it means there is no deviation. If the number of segments is 1, there may be a turning point or linear deviation, so all slopes can be further determined. If the standard deviation is below a certain critical value, it is judged as a linear deviation. If the number of segments is greater than 1, it indicates a nonlinear deviation. In the embodiment of the graph 340, segments 342-344 are generated because the slope at the segment junction exceeds the above range.

Next, we will explain the linear regression algorithm. We can use a linear function to approximate all depth values. represents the i-th depth value. The linear regression algorithm is to perform the optimization algorithm of the following mathematical formula 1. [Mathematical formula 1]

in is the depth value The corresponding position in the sampling direction, represents a linear equation, The purpose of Equation 1 is to find a straight line to approximate these depth values. represents the slope of the line. If the error calculated by equation 1 is less than a critical value and the variable If the error calculated by equation 1 is less than the critical value but the variable Outside the range -R~+R, it is expressed as a linear deviation. If the error calculated by Mathematical Formula 1 is greater than or equal to the critical value, it is expressed as a nonlinear deviation. After determining that it is a nonlinear deviation, the segment can be further determined, such as executing the following optimization algorithm. [Mathematical Formula 2]

Where k is a positive integer representing the boundary between two segments. is used to approximate the depth value in the first fragment, and the linear equation is used to approximate the depth value in the second segment. Mathematical formula 2 is to find the positive integer k and the variables in the two linear equations , so that the objective function of Mathematical Formula 2 has the smallest error. If the error calculated by Mathematical Formula 2 is greater than a second critical value, it means that there are more than 2 segments, and a new segment can be added to repeat the similar optimization algorithm until the error is less than the second critical value. In other words, it can be determined whether the rectangular depth image 300 has a linear deviation or a non-linear deviation based on the result of the linear regression algorithm.

In addition to the above-mentioned slope and linear regression methods, a person skilled in the art can use any algorithm to determine whether the depth value can be approximated as a horizontal line (no deviation), an oblique line (linear deviation) or multiple segments (non-linear deviation), and the present disclosure is not limited to the above-mentioned methods.

After detecting the deviation, compensation can be performed based on whether there is a deviation. If there is no deviation, no compensation is performed in step 350. If it is a linear deviation, linear compensation can be performed in step 204. Specifically, in the above method, the variables of the linear equation can be found. , for depth values The compensation can be written as If the slope method is used, all slopes can be calculated The average of As a result, the compensated depth value will be close to the horizontal line shown in the graph 320. represents the average of more than 300 pixels of the rectangular depth image, so the above compensation can be performed on all corresponding pixels. If it is a nonlinear deviation, nonlinear compensation can be performed in step 205. Specifically, the compensation in the first segment is similar to linear compensation and is written as ; The compensation in the second fragment is written as , and so on if there are more fragments. The depth value after compensation will be close to the horizontal line shown in graph 320. Similarly, due to the depth value It represents the average of more than 300 pixels of the rectangular depth image, so the above compensation can be performed on all corresponding pixels.

Please return to FIG. 2 . If the result of step 202 is no or after compensation in steps 204 and 205, an ideal data type (similar to graph 320 ) can be obtained in step 206 . In step 207 , the compensated rectangular depth image 300 can be post-processed, for example, the rectangular coordinates of the rectangular image can be converted into polar coordinates to become a circular image that conforms to the shape of the steel coil.

In the above-mentioned depth detection and compensation system, by rotating the object to be detected, the scanning range of the depth sensing module 130 does not need to cover the entire object to be detected, which can reduce the hardware requirements of the depth sensing module 130 or improve the scanning accuracy. In addition, since the object to be detected may rotate along a vertical axis or the speed is not fixed, resulting in deviations in the sensed depth, a method for detecting and compensating the deviation is also proposed herein.

Although the present invention has been disclosed as above by the embodiments, they are not intended to limit the present invention. Any person with ordinary knowledge in the relevant technical field can make some changes and modifications without departing from the spirit and scope of the present invention. Therefore, the protection scope of the present invention shall be defined by the scope of the attached patent application.

100: Depth detection and compensation system 110: Object to be detected 121,122: Rotation module 130: Depth sensing module 140: Calculation module 150,160: Axis 201~207: Step 300: Rectangular depth image 301: Sampling direction 302: Rotation direction 310: Sliding window 320,330,340: Graph 321,331,341: Depth value 322,332: Line 342~344: Clip 350: Step

FIG. 1 is a schematic diagram of a detection system according to an embodiment. FIG. 2 is a flow chart of a depth detection and compensation method according to an embodiment. FIG. 3 is a schematic diagram of a detection deviation according to an embodiment.

201~207: Steps

Claims (10)

A depth detection and compensation system includes: an object to be detected; a plurality of rotation modules for rotating the object to be detected around an axis, wherein the outline of the object to be detected viewed from the axis is a circle; a depth sensing module, disposed on one side of the object to be detected, for obtaining a rectangular depth image, wherein the rectangular depth image has a sampling direction and a rotation direction, and the scanning range of the depth sensing module is smaller than the diameter of the circle; and a calculation module, communicatively connected to the rotation modules and the depth sensing module, for applying a sliding window on the rectangular depth image, wherein the sliding window moves along the sampling direction to obtain a plurality of depth values, wherein the calculation module is used to determine whether the rectangular depth image has a deviation according to the depth values, and if so, to compensate for the deviation. A depth detection and compensation system as described in claim 1, wherein the length of the sliding window in the sampling direction is greater than 1, the length of the sliding window in the rotation direction is equal to the length of the rectangular depth image in the rotation direction, wherein the calculation module is used to execute a moving average algorithm based on the sliding window, and each position of the sliding window corresponds to one of the depth values. A depth detection and compensation system as described in claim 2, wherein the calculation module is also used to calculate the slope between every two adjacent depth values among the depth values, and determine whether the rectangular depth image has a linear deviation or a non-linear deviation based on whether the slope exceeds a range. A depth detection and compensation system as described in claim 2, wherein the computing module is further used to execute a linear regression algorithm based on the depth values, and to determine whether the rectangular depth image has a linear deviation or a non-linear deviation based on the result of the linear regression algorithm. A depth detection and compensation system as described in claim 1, wherein the object to be detected is a cylindrical steel coil. A depth detection and compensation method is applicable to a computer system, and the depth detection and compensation method includes: Rotating an object to be detected around an axis through multiple rotation modules, wherein the outline of the object to be detected viewed from the axis is circular; Obtaining a rectangular depth image through a depth sensing module, wherein the depth sensing module is set on one side of the object to be detected, the rectangular depth image has a sampling direction and a rotation direction, and the scanning range of the depth sensing module is smaller than the diameter of the circle; Applying a sliding window on the rectangular depth image, the sliding window moves along the sampling direction to obtain multiple depth values; and Judging whether the rectangular depth image has deviation according to the depth values, and if so, compensating for the deviation. The depth detection and compensation method as described in claim 6, wherein the length of the sliding window in the sampling direction is greater than 1, and the length of the sliding window in the rotation direction is equal to the length of the rectangular depth image in the rotation direction, and the depth detection and compensation method further includes: Executing a moving average algorithm based on the sliding window, each position of the sliding window corresponds to one of the depth values. The depth detection and compensation method as described in claim 7 further includes: Calculating the slope between every two adjacent depth values among the depth values, and judging whether the rectangular depth image has a linear deviation or a nonlinear deviation according to whether the slope exceeds a range. The depth detection and compensation method as described in claim 7 further includes: Executing a linear regression algorithm based on the depth values, and determining whether the rectangular depth image has a linear deviation or a non-linear deviation based on the result of the linear regression algorithm. A depth detection and compensation method as described in claim 6, wherein the object to be detected is a cylindrical steel coil.

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