Active laser probe

Abstract

The application relates to the technical field of three-coordinate measurement, and discloses an active laser measuring head. The laser measuring head comprises a laser emission module, an ambient light constant module, an acquisition module, a signal processing and control module and an interference suppression unit. The signal processing and control module is used for segmenting a workpiece and a detection table based on texture features, automatically focusing based on definition and resolution, and calculating a curvature variation trend based on pixel density of each detection area. The measurement result is checked in combination with the curvature and brightness of the current area, and the measurement, light supplement and focusing parameters of the next area are adaptively adjusted based on the curvature trend. The interference suppression unit outputs a predefined stabilized image signal to the signal processing and control module to suppress measurement errors caused by ambient light fluctuation. Through dynamic adjustment of ambient light, automatic focusing and parameter adaptation, the application overcomes measurement errors caused by overcutting / undercutting of curved surface edges, high reflectivity or dark materials, and is suitable for high-precision three-dimensional measurement of complex curved surfaces.

Description

Technical Field

[0001] This invention relates to the field of coordinate measuring machine technology, specifically to an active laser probe. Background Technology

[0002] When scanning curved surfaces, coordinate measuring machines (CMMs) laser probes exhibit inherent defects due to laser triangulation. Specifically, when inspecting curved workpieces with large inclination angles, the laser beam tends to exceed the actual contour at the surface edge, resulting in overcutting or undercutting. This leads to deviations in the coordinate data of the surface edge, affecting the measurement accuracy of geometric tolerances. Furthermore, the material of the curved workpiece also affects the inspection accuracy. For example, when scanning highly reflective surfaces, the laser is prone to specular reflection, causing the received light spot signal to be too strong or offset, resulting in coordinate calculation errors. Conversely, when scanning dark, light-absorbing surfaces, the laser is heavily absorbed, resulting in a weak signal, increased noise in the point cloud data, and surface imperfections, causing inspection interference. Summary of the Invention

[0003] To overcome the problems of edge error and material interference that existing laser probes easily cause when measuring curved workpieces, this application provides an active laser probe that can actively adapt to surface changes and material differences. The specific solution is as follows:

[0004] An active laser probe, comprising:

[0005] The laser emitting module projects a collimated linear laser beam onto the detection surface;

[0006] The ambient light constant module dynamically adjusts the ambient light on the detection surface to keep it constant.

[0007] The acquisition module acquires image information of the detection surface, including pixel density, texture features, brightness, resolution, and sharpness of the image.

[0008] The signal processing and control module, connected to the laser emission module, the ambient light constant module, and the acquisition module respectively, is configured to execute:

[0009] The detection surface is divided into regions based on the detection accuracy of the laser emission module, and the workpiece and the detection table are segmented based on the texture features obtained by the acquisition module.

[0010] The image is determined to be out of focus or in focus based on changes in resolution and sharpness, and a focus mode is triggered to achieve automatic focusing on the detection area.

[0011] The curvature is calculated based on the pixel density of each detection area to obtain the curvature change trend of the entire detection surface;

[0012] The measurement results of the current detection area are checked by combining the curvature and brightness of the current detection area; and the measurement parameters, supplementary lighting parameters and focus parameters of the next detection area are adaptively adjusted based on the curvature change trend.

[0013] The interference suppression unit, connected to the ambient light constant module, the acquisition module, and the signal processing and control module, is configured as follows:

[0014] Receives image signal strength output from the acquisition module;

[0015] Receive ambient light status output from the ambient light constant module;

[0016] When the ambient light status output indicates that the ambient light intensity is below or above a preset range, a predefined stabilized image signal is output to the signal processing and control module to suppress measurement errors caused by ambient light fluctuations.

[0017] Preferably, the signal processing and control module includes an image processing unit, a focus adjustment unit, and a processor;

[0018] The image processing unit is connected to the acquisition module and the processor respectively. Based on the image information acquired by the acquisition module, the image processing unit analyzes the sharpness and resolution, and extracts the pixel density and texture features of the image.

[0019] The processor is connected to the interference suppression unit, the focus adjustment unit, the ambient light constant module, and the laser emission module. The processor calculates the curvature change trend based on the pixel density extracted by the image processing unit, and controls the focus adjustment unit based on the curvature change trend and the corresponding sharpness. Based on the interference suppression unit, it outputs a predefined stabilized image signal to suppress measurement errors caused by ambient light fluctuations. It also controls the ambient light constant module based on the ambient light intensity, brightness, and curvature change trend.

[0020] Preferably, the acquisition module includes a global shutter CMOS sensor and an ambient light sensor;

[0021] The global shutter CMOS sensor is connected to the image processing unit. The photosensitive array of the global shutter CMOS sensor has embedded dual photodiodes covered by microlenses, which constitute a phase detection autofocus pixel unit.

[0022] The global shutter CMOS sensor and the ambient light sensor move synchronously with the laser emission module to acquire image information and reflected light information of the unit detection area in real time.

[0023] The ambient light sensor is connected to the interference suppression unit and the processor, respectively.

[0024] Preferably, the ambient light constant module is an adjustable LED array;

[0025] The LED array is disposed on the periphery of the surface to be detected and is connected to the interference suppression unit and the processor respectively. The LED array provides corresponding ambient light to the unit detection area of ​​the detection surface based on the instructions of the processor.

[0026] Preferably, the focus adjustment unit is based on a sharpness contrast detection method, which determines the optimal focus position by finding the extreme value of image contrast.

[0027] Preferably, the specific implementation method for determining the image's focus or defocus status based on changes in resolution and sharpness is as follows:

[0028] The image processing unit calculates the gradient magnitude or frequency domain features of the image as a sharpness evaluation function.

[0029] When the sharpness evaluation function value is lower than the set threshold, it is determined to be out of focus and the focus mode is triggered;

[0030] The lens group is moved by the focusing adjustment unit to find the extreme point of the sharpness evaluation function in order to achieve focus.

[0031] The preferred implementation scheme for segmenting the workpiece contour based on image texture differences is as follows:

[0032] 1) Collect typical texture features of the testing platform and at least one workpiece to be tested, and establish a typical texture feature library containing the background texture features of the testing platform and the texture features of each workpiece to be tested.

[0033] 2) Real-time acquisition of images of the surface to be inspected, location and extraction of texture features of the region of interest (ROI) in the image, comparison and matching of the texture features of the ROI with the typical texture feature library, and identification of the scene type corresponding to the current ROI as inspection station or workpiece to be inspected;

[0034] 3) Based on the matching results of step 2), select the edge detection algorithm corresponding to the current scene type and perform edge detection on the ROI.

[0035] Preferably, the measurement parameters include detection speed, detection angle, detection light energy, and resource allocation for synchronous detection data processing.

[0036] The supplementary lighting parameters include light intensity parameters, color parameters, and supplementary lighting angle parameters;

[0037] The focusing parameters include the number of detection points and the distribution of detection points.

[0038] Preferably, the specific method for adaptively adjusting the measurement parameters, supplementary lighting parameters, and focus parameters of the next detection area is as follows:

[0039] When the curvature of the detection surface increases (i.e., the surface curvature changes more drastically), the measurement angle, measurement distance, supplementary lighting angle, and light intensity parameters are adjusted accordingly, and the number of detection points is increased to obtain denser and more comprehensive data.

[0040] To obtain denser and more comprehensive data; when the curvature change trend decreases (i.e. the surface is smoother), the above parameters are reduced or decreased to avoid redundant detection;

[0041] The detection points for focusing are dynamically set up based on the curvature change trend to ensure accurate focusing in areas with significant curvature changes;

[0042] Based on the curvature change trend, the scanning speed is adaptively increased or decreased, and the computing resources are increased or decreased accordingly. The laser energy compensation strategy is also activated accordingly to maintain the constant signal quality of the detection beam.

[0043] Compared with the prior art, the beneficial effects of this application are as follows:

[0044] For a curved workpiece to be tested, when the image obtained by image scanning is converted into a plane, the density of pixels obtained by scanning with different curvatures is different. The greater the curvature of the corresponding surface during acquisition, the greater the pixel density of the acquired plane. This application obtains the image information of each detection area on the detection surface through the acquisition module, and calculates the curvature corresponding to each detection area based on the pixel density of each detection area, and then fits the curvature change trend of the entire detection surface. Based on the acquired curvature change trend, the signal processing and control module adaptively adjusts the measurement parameters (specifically, the detection speed, measurement angle, laser energy, and corresponding data processing resource allocation), supplementary lighting parameters (specifically, the intensity, color or color temperature, and angle of the supplementary lighting), and focusing parameters (specifically, the number and distribution of the focusing detection points) for the next detection area. When measuring high curvature or steep surfaces, the system automatically reduces the scanning speed, increases the number of detection points and the light intensity to ensure that the laser beam can cover the edge area and accurately capture surface details, avoiding data loss or coordinate calculation errors caused by reflected light exceeding the camera's field of view. This improves the accuracy of surface measurement while reducing the scanning speed and allocating more computing resources to improve detection efficiency. Conversely, in flat areas, the system reduces parameters to avoid redundant detection and improve efficiency. This enables the probe to adaptively adjust according to the curvature change trend, solving the problems of traditional laser probes when measuring steep curved surfaces, where the laser beam is prone to exceeding the camera's field of view due to excessive tilt angle, resulting in missing point cloud data or incorrect coordinate calculations, and the generated curved surface showing depressions or breaks; and when measuring gentle curved surfaces, redundant detection leads to reduced detection efficiency. This improves both measurement accuracy and measurement efficiency.

[0045] This application establishes a typical texture feature library and utilizes an image processing unit to extract texture features of the region of interest (ROI) in the image of the surface to be inspected from the acquisition module in real time. These features are then compared and matched with the feature library, and an appropriate edge detection algorithm is selected to segment the workpiece and the inspection platform. This ensures the accuracy of contour recognition and avoids the common problem of traditional laser probes misjudging edges in complex backgrounds, especially when the workpiece and the inspection platform have similar textures. The inability to accurately distinguish the boundary between the workpiece and the inspection platform often leads to overcutting (i.e., exceeding the actual contour) or undercutting (i.e., not completely covering the edge) of the curved surface edge, causing deviations in the curved surface edge coordinate data and affecting the accuracy of geometric tolerance measurement. For example, when measuring small precision parts, if the texture difference between the part and the inspection platform is small, traditional probes are prone to misjudging the contour and missing edge details. The probe described in this application, based on texture feature segmentation technology, can accurately identify boundaries in complex scenes and effectively suppress contour recognition errors caused by background interference. This not only avoids overcutting / undercutting of edges.

[0046] This application utilizes an ambient light constant-light module comprised of an ambient light sensor and an adjustable LED array within the acquisition module. This module works in conjunction with the ambient light sensor and an interference suppression unit. The ambient light sensor detects the light intensity of the detection surface in real time and transmits the data to the processor. The processor, considering brightness levels, sends commands to the LED array to adjust the light intensity, color temperature, and supplementary lighting angle, ensuring uniform and stable illumination of the detection surface and maintaining constant ambient light. When the ambient light supplementary output exceeds a preset range, the interference suppression unit outputs a stabilized image signal to the signal processing and control module to suppress errors caused by light fluctuations. Through this ambient light control mechanism, local illumination is compensated or reduced in real time during the detection process, ensuring consistent image signal quality. This addresses the problems of traditional laser probes, where specular reflection of the laser easily occurs when measuring highly reflective materials, leading to excessively strong or offset light spot signals received by the camera and coordinate calculation errors; and where significant absorption of the laser results in weak signals, increased noise in point cloud data, and substantial detection interference when measuring dark, light-absorbing materials.

[0047] The image information of the detection surface in this application is acquired by a global shutter CMOS sensor in the acquisition module. The photosensitive array is embedded with dual photodiodes covered by microlenses, which constitute a phase detection autofocus (PDAF) pixel unit. The image processing unit of the signal processing and control module calculates the image gradient amplitude or frequency domain features as a sharpness evaluation function. When the function value is lower than the threshold, the focusing mode is triggered. At this time, the PDAF unit quickly determines the direction and degree of defocus, and the focusing adjustment unit drives the lens group to move. The sharpness extreme point is found by contrast detection method to complete the focusing. This solves the problem that when the curvature of the detection surface changes, the traditional laser probe is prone to defocusing when measuring curved surfaces, which leads to image blurring and edge extraction failure. It requires manual refocusing, which not only affects efficiency but may also introduce operational errors.

[0048] The laser probe described in this application is equipped with an interference suppression unit. The interference suppression unit is connected to the ambient light constant module, the acquisition module and the signal processing and control module respectively. It receives the image signal intensity output and the ambient light status output in real time. When the ambient light is abnormal, it outputs a predefined stable image signal to the processing module to suppress the error caused by light fluctuation.

[0049] This application dynamically adjusts the scanning speed, focusing parameters, and computing resources based on the curvature change trend of the detection surface to avoid measurement distortion caused by spot loss in steep curved surfaces or deep cavity structures; it intelligently segments the workpiece and background based on texture features to effectively suppress edge over-cutting / under-cutting phenomena and improve contour recognition accuracy; it adapts to measurement scenarios with highly reflective or dark materials through dynamic ambient light compensation and control; and it effectively improves the accuracy, success rate, and stability of complex curved surface measurement by combining autofocus and adaptive parameter adjustment; thus providing an active laser probe capable of detecting complex curved surfaces. Attached Figure Description

[0050] Figure 1 This is a schematic diagram of the principle framework of the active laser probe of the present invention;

[0051] Figure 2 This is a schematic diagram of the operation of the active laser probe in the embodiments of this application;

[0052] In the attached diagram: 1. Laser emission module; 2. Global shutter CMOS sensor; 3. Ambient light sensor; 4. Intelligent LED fill light; 5. Inspection stage; 6. Workpiece to be tested. Detailed Implementation

[0053] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0054] like Figure 1 An active laser probe, as shown, includes:

[0055] The laser emitting module projects a collimated linear laser beam onto the detection surface;

[0056] The ambient light constant module dynamically adjusts the ambient light on the detection surface to keep it constant.

[0057] The acquisition module acquires image information of the detection surface, including pixel density, texture features, brightness, resolution, and sharpness of the image.

[0058] The signal processing and control module is connected to the laser emission module, the ambient light constant module, and the acquisition module, respectively. The signal processing and control module is used to perform the following:

[0059] Based on the detection accuracy of the laser emission module, the detection surface is divided into regions, and the workpiece and the detection table are segmented based on the differences in texture features;

[0060] The image is determined to be out of focus or in focus based on changes in resolution and sharpness, and a focus mode is triggered to achieve automatic focusing on the detection area.

[0061] Based on the pixel density of each detection area, the curvature of each detection area is calculated to obtain the curvature change trend of the entire detection surface.

[0062] The measurement results of the current detection area are checked by combining the curvature and brightness of the current detection area; and the measurement parameters, supplementary lighting parameters and focus parameters of the next detection area are adaptively adjusted based on the curvature change trend.

[0063] The interference suppression unit, connected to the ambient light constant module, the acquisition module, and the signal processing and control module, is configured as follows:

[0064] Receives image signal strength output from the acquisition module;

[0065] Receive ambient light status output from the ambient light constant module;

[0066] When the ambient light status output indicates that the ambient light intensity is below or above a preset range, a predefined stabilized image signal is output to the signal processing and control module to suppress measurement errors caused by ambient light fluctuations; when the signal intensity in a certain area is detected to be below a threshold, the laser emission module is controlled to adjust the output current of the laser driver to increase the laser power and achieve laser energy compensation.

[0067] It should be noted that:

[0068] In this application, the active laser probe also includes a housing, and the laser emission module, the ambient light constant module, and the signal processing and control module are respectively installed inside the housing.

[0069] In this application, the curvature calculation is based on the pixel distribution density of the laser stripes in the image, and the specific curvature calculation formula is as follows:

[0070] ;

[0071] in:

[0072] ρ represents the local curvature variation trend (dimensionless).

[0073] Δx represents the horizontal pixel displacement of the laser stripe within a unit detection area in the image;

[0074] Δy represents the vertical pixel displacement of the laser stripe within the unit detection area in the image;

[0075] Δx and Δy are calculated based on the angular change between adjacent elements;

[0076] k is the calibration coefficient, which is obtained by fitting a reference surface with a known radius of curvature. The method for determining k is as follows: a standard sphere or cylinder with a known radius of curvature is used as a calibration block, and the laser probe is used to scan it to obtain the pixel displacement ratio Δx / Δy at multiple positions; based on the actual curvature 1 / R of the calibration block and the measured pixel displacement ratio Δx / Δy, a linear fit is performed using the least squares method, and the slope of the fitted line is the calibration coefficient k.

[0077] In one embodiment of this application, when the probe is working, the laser emitting module 1 projects a linear laser beam onto the workpiece 6 to be tested, which is placed on the testing stage 5. The global shutter type CMOS sensor 2 and the ambient light sensor 3 move synchronously with the laser emitting module 1 and are used to acquire images and ambient light information of the unit testing area, respectively.

[0078] The signal processing and control module mainly performs the following functions: segmenting the workpiece and the inspection stage based on texture features, controlling focus based on the sharpness evaluation function, calculating curvature trends based on pixel density, and adaptively adjusting parameters for the next area. The ambient light constant module dynamically supplements light using intelligent LED supplementary lighting 4, and the interference suppression unit outputs a stable signal when light intensity is abnormal. This achieves high-precision, adaptive measurement of complex curved surfaces, effectively overcoming the limitations of traditional laser probes. The specific implementation process is as follows:

[0079] The image processing unit pre-stores the texture feature library of the detection stage 5 and the workpiece 6 to be tested. The image processing unit extracts the texture features of the acquired image in real time, matches them with the texture feature library, selects the optimal edge detection algorithm (such as the Canny algorithm), and accurately segments the contours of the workpiece 6 to be tested and the detection stage 5 to avoid edge measurement errors.

[0080] The gradient magnitude of the image is calculated as a sharpness evaluation metric. When the metric value is below a threshold, it is determined to be out of focus. The processor controls the focus adjustment unit to drive the lens group to move in order to find the sharpness extreme point and achieve rapid focusing. The PDAF pixel unit in the CMOS sensor provides an initial estimate of the direction and degree of out-of-focus, helping to speed up the focusing process.

[0081] Analyze the pixel distribution density of the laser stripes in the image. Then, fit the curvature change trend of the entire detection surface based on the pixel distribution density in the image.

[0082] The reliability of the measurement results is verified based on the curvature ρ and brightness of the current region. Furthermore, based on the curvature change trend, the parameters of the next detection region are adaptively adjusted in advance.

[0083] When the curvature trend increases, reduce the scanning speed, increase the detection angle, and allocate more data processing resources (such as enabling multithreading); the opposite is true when the curvature trend decreases.

[0084] Feedback from the ambient light sensor 3 allows the processor to control the drive circuit of the intelligent LED fill light 4, adjusting its luminous intensity, color temperature, and illumination angle to supplement light in dark areas and reduce light in bright areas, thus maintaining uniform and stable illumination across the entire detection surface.

[0085] In detection areas where curvature tends to increase, the number and density of focus detection points are increased to improve focusing accuracy.

[0086] When the intensity of the reflected signal is detected to be below a threshold, the processor adjusts the current of the laser driver to increase the laser power and compensate for the signal attenuation.

[0087] The ambient light constant module consists of multiple sets of intelligent LED fill lights 4 arranged around the workpiece to be tested. The light intensity, color temperature and angle of each set of intelligent LED fill lights 4 can be adjusted independently.

[0088] Furthermore, the signal processing and control module includes an image processing unit, a focus adjustment unit, and a processor;

[0089] The image processing unit is connected to the acquisition module and the processor respectively. Based on the image information acquired by the acquisition module, the image processing unit analyzes the sharpness and resolution, and extracts the pixel density and texture features of the image.

[0090] The processor is connected to the interference suppression unit, the focus adjustment unit, the ambient light constant module, and the laser emission module. The processor calculates the curvature change trend based on the pixel density extracted by the image processing unit, and controls the focus adjustment unit based on the curvature change trend and the corresponding sharpness. Based on the interference suppression unit, it outputs a predefined stabilized image signal to suppress measurement errors caused by ambient light fluctuations. It also controls the ambient light constant module based on the ambient light intensity, brightness, and curvature change trend.

[0091] It should be noted that in one embodiment of this application, the FPGA used in the signal processing and control module is a Xilinx Artix-7 series XC7A100T, wherein the FPGA is configured with image edge contrast calculation, defocus or focus judgment and focus control functions; wherein the processor is an ARM Cortex-M7.

[0092] Furthermore, the acquisition module includes a global shutter CMOS sensor and an ambient light sensor;

[0093] As attached Figure 2 As shown, the global shutter CMOS sensor 2 is connected to the image processing unit. The photosensitive array of the global shutter CMOS sensor has embedded dual photodiodes covered by microlenses. By detecting the phase difference in the image, it determines the direction and degree of defocus, thereby assisting in the realization of a fast autofocus unit, forming a phase detection autofocus (PDAF) pixel unit. The laser emission module 1 is set above the detection stage 5. The global shutter CMOS sensor 2 and the ambient light sensor 3 are located on both sides of the laser emission module 1, moving synchronously with the laser emission module 1. All three are electrically connected to the signal processing and control module to acquire the image information of the unit detection area of ​​the detection surface of the workpiece 6 and the reflected light information of the image in real time.

[0094] The ambient light sensor is connected to the interference suppression unit and the processor, respectively.

[0095] It should be noted that, in one embodiment of this application, the global shutter CMOS sensor and the ambient light sensor are located on one side of the laser emission module; wherein the specification / model of the ambient light sensor is: AMS TSL2561.

[0096] The phase detection autofocus (PDAF) pixel unit determines the direction and degree of defocus by detecting phase differences in the image, and outputs a signal to the focus adjustment unit to drive the lens group to make fine adjustments and achieve focus. The processor uses an ARM Cortex-M7 core, and the image processing unit uses a Xilinx Artix-7 FPGA to realize parallel processing of image processing and control logic.

[0097] Furthermore, the ambient light constant module is an adjustable LED array;

[0098] The LED array is disposed around the periphery of the surface to be detected and is connected to both the interference suppression unit and the processor. Based on instructions from the processor, the LED array provides corresponding ambient lighting to a unit detection area of ​​the surface. The output of the LED array is dynamically adjusted based on these processor instructions to suppress ambient light fluctuations and ensure a good image signal-to-noise ratio.

[0099] It should be noted that:

[0100] In one embodiment of this application, at least one set of smart LEDs is arranged around the detection surface. Each set includes two color-adjustable smart LED fill lights 4, and the smart LED fill lights 4 are all driven by constant current. Each smart LED fill light 4 is installed on a lamp holder with three-axis adjustment and angle adjustment. During measurement, the ambient light sensor detects the ambient light intensity in real time and converts it into a digital signal and transmits it to the processor.

[0101] The processor is connected to the controller of the intelligent LED fill light 4. Based on the difference in brightness in different detection areas, it controls the corresponding intelligent LED fill light 4 to achieve local supplemental lighting or dimming, so as to maintain the dynamic stability of the light intensity.

[0102] Based on the image information acquired by the global shutter CMOS sensor, the corresponding dimming intensity, color adjustment parameters, and fill light angle are calculated.

[0103] In this application, ambient light fluctuations are suppressed, which on the one hand solves the detection error caused by uneven light intensity on the detection surface due to curvature changes; on the other hand, by ensuring that the image signal-to-noise ratio is at a good level, the problem of signal-to-noise ratio drop in the focusing sensor is avoided, thereby improving focusing speed and accuracy.

[0104] Furthermore, the focus adjustment unit determines the optimal focus position by finding the extreme value of image contrast based on the contrast detection method of sharpness.

[0105] Furthermore, the specific implementation method for determining the out-of-focus or in-focus state of an image based on changes in resolution and sharpness is as follows:

[0106] The image processing unit calculates the gradient magnitude or frequency domain features of the image as a sharpness evaluation function.

[0107] When the sharpness evaluation function value is lower than the set threshold, it is determined to be out of focus and the focus mode is triggered;

[0108] The lens group is moved by the focusing adjustment unit to find the extreme point of the sharpness evaluation function in order to achieve focus.

[0109] Furthermore, the specific implementation scheme for segmenting the contour between the workpiece 6 and the inspection stage 5 based on image texture differences is as follows:

[0110] 1) Collect typical texture features of the testing platform and at least one workpiece to be tested, and establish a typical texture feature library containing the background texture features of the testing platform and the texture features of each workpiece to be tested.

[0111] 2) Real-time acquisition of images of the surface to be inspected, location and extraction of texture features of the region of interest (ROI) in the image, comparison and matching of the texture features of the ROI with the typical texture feature library, and identification of the scene type corresponding to the current ROI as inspection station or workpiece to be inspected;

[0112] 3) Based on the matching results of step 2), select the edge detection algorithm corresponding to the current scene type and perform edge detection on the ROI.

[0113] Furthermore, the measurement parameters include detection speed, detection angle, detection light energy, and resource allocation for synchronous detection data processing.

[0114] The supplementary lighting parameters include light intensity parameters, color parameters, and supplementary lighting angle parameters;

[0115] The focusing parameters include the number of detection points and the distribution of detection points.

[0116] Furthermore, the specific method for adaptively adjusting the measurement parameters, supplementary lighting parameters, and focus parameters of the next detection area is as follows:

[0117] When the curvature of the detection surface increases (i.e., the surface curvature changes more drastically), the measurement angle, measurement distance, supplementary lighting angle, and light intensity parameters are adjusted accordingly, and the number of detection points is increased to obtain denser and more comprehensive data.

[0118] To obtain denser and more comprehensive data; when the curvature change trend decreases (i.e. the surface is smoother), the above parameters are reduced or decreased to avoid redundant detection.

[0119] Specifically: When the curvature change trend increases, the distance between the probe and the detection surface is shortened to accommodate fluctuations in local protrusions and depressions; when the curvature change trend increases, the normal direction at each point on the surface changes drastically. If the measurement angle is fixed, the angle between the laser beam and the normal of some curved areas may be too large, resulting in attenuation or loss of the reflected signal. Therefore, when the curvature change trend increases, the measurement angle of the laser probe is dynamically adjusted to keep the angle between the incident direction of the laser beam and the normal of the curved surface at approximately 45°, ensuring stable reflected signal intensity. At the same time, the frequency of angle adjustment is increased to avoid missing details caused by angle jumps; when the curvature increases, local areas of the curved surface are subjected to upward... Angle variations and fixed supplementary lighting angles may lead to backlighting or overexposure in some areas, affecting the edge clarity of the laser spot. Therefore, the angle of the supplementary light source is adjusted according to the reflectivity of the curved surface to ensure that the measurement angle remains at approximately 45°. When the curvature increases, there may be more signal attenuation points or signal saturation points. Therefore, in this application, for highly reflective detection areas, such as the convex apex of a metal curved surface, the light intensity is adjusted accordingly by real-time monitoring of the reflected signal intensity to avoid edge blurring caused by overexposure of the spot and to ensure the edge extraction accuracy of the size measurement. For concave detection areas, corresponding compensation is performed to ensure that the amplitude of the reflected signal is not lower than the lower limit of the sensor sensitivity.

[0120] The detection points for focusing are dynamically set up based on the curvature change trend to ensure accurate focusing in areas with significant curvature changes.

[0121] Based on the curvature change trend, the scanning speed is adaptively increased or decreased, and computing resources are increased or decreased accordingly. A laser energy compensation strategy is also activated to maintain constant signal quality of the detection beam. It should be noted that:

[0122] In one embodiment of this application, the laser power compensation strategy is as follows: when the signal strength of a certain detection area is detected to be lower than a threshold, the output current of the laser driver is automatically adjusted to increase the laser power.

[0123] In this application, for high curvature detection areas, the system automatically reduces the scanning speed and increases processing resources to ensure the integrity of data acquisition. For example, when the detected curvature change rate exceeds a set threshold, the scanning speed is automatically adjusted from the baseline value of 100 mm / s to 30 mm / s, and the data processing thread is increased from single-threaded to dual-threaded to increase transmission and computing resources. Simultaneously, the number of focusing detection points is increased, and detection points are deployed according to the curvature change trend to improve focusing accuracy. For situations where the detection light is absorbed or the signal is weak due to shadows on the curvature-changing detection surface, the consistency of signal quality is ensured by adjusting the intensity and color of the compensation light and adjusting the scanning laser power. This addresses the problems of spot loss and data missing in high curvature surface measurement. Real-time ambient light monitoring and compensation technology is employed to enable the system to maintain stable measurement performance under varying ambient light conditions, improving the signal-to-noise ratio.

[0124] In this application, the focusing system employs a hill-climbing algorithm to precisely adjust the lens position based on sharpness indicators, ensuring image sharpness. The active laser probe described in this application achieves high-precision measurements in complex environments and exhibits excellent adaptability to surfaces with varying curvatures and materials.

[0125] In this application, the ambient light illuminance of each detection area on the detection surface is first obtained through an ambient light sensor. The signal processing and control module calculates the required compensation light intensity based on the illuminance value. Specifically, the detection surface is divided into multiple uniform detection areas, and the curvature of each detection area is calculated individually to obtain the curvature change trend of the entire detection surface. Based on the calculated light intensity difference between each detection area, the number, distance, color, and angle of the compensation power supply are controlled accordingly. For light intensities below a threshold, compensation is performed; for light intensities exceeding the threshold, light reduction processing is performed based on the difference. During supplementary lighting, light reduction is achieved by turning off the number of compensation power supplies or moving the distance of the compensation power supplies. The output of the LED array is adjusted through a PID controller. The DSP processor calculates the surface curvature, texture features, and sharpness index based on image features and adjusts the laser power, scanning speed, and focusing parameters accordingly.

[0126] The above embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the present invention.

Claims

1. An active laser probe, characterized in that, include: The laser emitting module projects a collimated linear laser beam onto the detection surface; The ambient light constant module dynamically adjusts the ambient light on the detection surface to keep it constant. The acquisition module acquires image information of the detection surface, including pixel density, texture features, brightness, resolution, and sharpness of the image. The signal processing and control module, connected to the laser emission module, the ambient light constant module, and the acquisition module respectively, is configured to execute: The detection surface is divided into regions based on the detection accuracy of the laser emission module, and the workpiece and the detection table are segmented based on texture features. Based on changes in resolution and sharpness, the system determines whether the image is out of focus or in focus, and triggers autofocus. The curvature is calculated based on the pixel density of each detection area to obtain the curvature change trend of the detection surface; Verify the measurement results of the current detection area by combining the curvature and brightness of the current detection area; And based on the curvature change trend, the measurement parameters, supplementary lighting parameters, and focusing parameters of the next detection area are adaptively adjusted; The interference suppression unit, connected to the ambient light constant module, the acquisition module, and the signal processing and control module, is configured as follows: Receives image signal intensity output from the acquisition module and ambient light status output from the ambient light constant module; When the ambient light status output indicates that the ambient light intensity is below or above a preset range, a predefined stabilized image signal is output to the signal processing and control module to suppress measurement errors caused by ambient light fluctuations.

2. The active laser probe according to claim 1, characterized in that, The signal processing and control module includes an image processing unit, a focus adjustment unit, and a processor; The image processing unit is connected to the acquisition module and the processor respectively. Based on the image information acquired by the acquisition module, the image processing unit analyzes the sharpness and resolution, and extracts the pixel density and texture features of the image. The processor is connected to the interference suppression unit, the focus adjustment unit, the ambient light constant module, and the laser emission module respectively. The processor calculates the curvature change trend based on the pixel density extracted by the image processing unit, and controls the focus adjustment unit based on the curvature change trend and the corresponding sharpness. Based on the interference suppression unit, a predefined stable image signal is output to suppress measurement errors caused by ambient light fluctuations; and an ambient light constant control module is used based on the trends of ambient light intensity, brightness, and curvature changes.

3. An active laser probe according to claim 2, characterized in that, The acquisition module includes a global shutter CMOS sensor and an ambient light sensor; The global shutter CMOS sensor is connected to the image processing unit. The photosensitive array of the global shutter CMOS sensor has embedded dual photodiodes covered by microlenses, which constitute a phase detection autofocus pixel unit. The global shutter CMOS sensor and the ambient light sensor move synchronously with the laser emission module to acquire image information and reflected light information of the unit detection area in real time. The ambient light sensor is connected to the interference suppression unit and the processor, respectively.

4. An active laser probe according to claim 2, characterized in that, The ambient light constant module is an adjustable LED array; The LED array is disposed on the periphery of the surface to be detected and is connected to the interference suppression unit and the processor respectively. The LED array provides corresponding ambient light to the unit detection area of ​​the detection surface based on the instructions of the processor.

5. An active laser probe according to claim 2, characterized in that, The focus adjustment unit is based on a sharpness contrast detection method, which determines the optimal focus position by finding the extreme value of image contrast.

6. An active laser probe according to claim 2, characterized in that, The specific implementation method for determining the out-of-focus or in-focus state of an image based on changes in resolution and sharpness is as follows: The image processing unit calculates the gradient magnitude or frequency domain features of the image as a sharpness evaluation function. When the sharpness evaluation function value is lower than the set threshold, it is determined to be out of focus and the focus mode is triggered; The lens group is moved by the focusing adjustment unit to find the extreme point of the sharpness evaluation function in order to achieve focus.

7. An active laser probe according to claim 1, characterized in that, The specific implementation scheme for workpiece contour segmentation based on image texture difference is as follows: 1) Collect typical texture features of the testing platform and at least one workpiece to be tested, and establish a typical texture feature library containing the background texture features of the testing platform and the texture features of each workpiece to be tested. 2) Real-time acquisition of images of the surface to be inspected, location and extraction of texture features of the region of interest in the image, comparison and matching of the texture features of the region of interest with the typical texture feature library, and identification of the scene type corresponding to the current region of interest as inspection station or workpiece to be inspected; 3) Based on the matching results of step 2), select the edge detection algorithm corresponding to the current scene type and perform edge detection on the region of interest.

8. An active laser probe according to claim 2, characterized in that, The measurement parameters include detection speed, detection angle, detection light energy, and resource allocation for synchronous detection data processing. The supplementary lighting parameters include light intensity parameters, color parameters, and supplementary lighting angle parameters; The focusing parameters include the number of detection points and the distribution of the detection points.

9. An active laser probe according to claim 8, characterized in that, The specific method for adaptively adjusting the measurement parameters, supplementary lighting parameters, and focus parameters of the next detection area is as follows: When the curvature of the detection surface increases, the measurement angle, measurement distance, supplementary lighting angle, and light intensity parameters are adjusted accordingly, and the number of detection points is increased to obtain denser and more comprehensive data; when the curvature decreases, the above parameters are reduced to avoid redundant detection. The detection points for focusing are dynamically set up based on the curvature change trend to ensure accurate focusing in areas with significant curvature changes; Based on the curvature change trend, the scanning speed is adaptively increased or decreased, and the computing resources are increased or decreased accordingly. The laser energy compensation strategy is also activated accordingly to maintain the constant signal quality of the detection beam.

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