42 results about "Texture recognition" patented technology

The invention discloses a visual tactile sensor fused with an optical waveguide and a preparation method thereof, and belongs to the technical field of intelligent tactile sensing. The visual tactile sensor comprises a fixed shell, and the top end of the fixed shell is open; the composite tactile sensing layer is arranged at the opening position of the fixed shell, and the composite tactile sensing layer is sequentially composed of a shading layer, a reflecting layer, a red-green spaced fluorescent layer, an elastic deformation layer and a supporting layer from top to bottom; and the optical lighting and sensing component comprises a blue LED light bar, an array light intensity sensor and a camera, the blue LED light bar and the array light intensity sensor are arranged on the two corresponding sides of the composite touch sensing layer respectively, and the camera is arranged at the bottom in the fixed shell. Through fusion of rapid and stable light intensity detection characteristics of optical waveguide sensing and pressure distribution detection capability of the visual tactile sensor, high-resolution space and texture identification and high-sensitivity mechanical response detection are simultaneously realized in a single sensor architecture.

The application discloses a flexible tactile sensing and feedback system and method based on texture recognition, which comprises an electrostatic flexible tactile sensor, a feedback actuator and a tactile feedback control circuit, the electrostatic flexible tactile sensor comprises a tactile interaction film for acquiring texture features and a pressure sensor for texture feature conversion; the feedback actuator completes tactile feedback of the texture feature signal acquired by the flexible sensor in the form of vibration; the tactile control system is mainly connected with the electrostatic flexible tactile sensor and the tactile feedback actuator through the feedback control circuit; the tactile feedback actuator has a stimulation reinforcement structure on the contact surface with the human body, which can further enhance the fine feedback of the tactile feeling. The application can effectively identify the texture features and complete the feedback of the fine tactile feeling in the form of vibration, provides effective assistance for the tactile recovery of the disabled, and has important application value in the game industry, especially in the development of virtual reality technology.

A texture recognition module. The texture recognition module includes: a substrate (1); a driving circuit layer (9) located on one side of the substrate, including a plurality of arrayed driving transistors (12); a first insulating layer (5) located on the side of the driving circuit layer away from the substrate, including a plurality of first vias (13) penetrating the thickness of the first insulating layer; a plurality of photoelectric conversion units (15) located on the side of the first insulating layer away from the driving circuit layer, which are in one-to-one contact with the first electrode of the driving transistor through the first vias; a second insulating layer (16) located on the side of the first insulating layer away from the substrate, including a plurality of second vias (17) corresponding to the photoelectric conversion units; the distance between the edge of the second via and the edge of the surface of the photoelectric conversion unit away from the substrate is less than or equal to a first preset value; a plurality of first electrodes (18) located on the side of the photoelectric conversion unit away from the driving circuit layer; each first electrode covers the photoelectric conversion unit in the second via.

This invention discloses a method and system for detecting coating defects on the surface of rollers based on image generation, belonging to the field of industrial product quality inspection technology. The method includes the following steps: acquiring continuous image data of the roller during a stable rotational speed phase and a sudden increase in rotational speed, recording the start time of the speed change, and generating rotational speed change identification information; organizing the continuous images in chronological order based on the rotational speed change identification information, comparing the brightness distribution of the images before and after the speed change, extracting areas of sudden brightness increase, and generating a set of bright areas. This invention achieves precise correspondence between image acquisition and roller operating state by introducing rotational speed change identification information, eliminating interference from sudden changes in illumination on texture recognition; and through dynamic backtracking of the texture real image set and peeling risk information, it achieves continuous tracking and adaptive detection of coating defects, improving recognition stability and detection reliability.

The invention relates to the technical field of computer vision and artificial intelligence, and particularly discloses a bird's nest texture AI identification verification system and method, and the system comprises an image collection module, an AI processing module, a database module, and a verification interaction module. The image acquisition module acquires a texture image by using high-precision equipment; the AI processing module extracts features, trains a model and matches the features; the database module stores data in a cloud distributed manner; and the verification interaction module supports multi-terminal verification. The method comprises the steps of production end collection and filing, AI model training optimization and terminal verification operation. The system takes the natural texture of the cubilose as an identifier, avoids counterfeiting, does not need a paper label, is high in verification accuracy, is simple to operate by consumers, and is suitable for multiple scenes.

The application provides a large-scale satellite image automatic registration and enhancement method based on adaptive multi-source fusion, comprising: performing orthorectification and multi-resolution unified projection on a multi-source original data set obtained to obtain a coarse registration image set; calculating a local tensor structure of the coarse registration image set, performing repeated texture recognition, generating a final structure representation and a structure reliability; performing regional registration to obtain a full-image dense deformation field, then performing image transformation to be aligned and registration uncertainty estimation to obtain a high-precision registration image and structure registration uncertainty estimation; combining a pseudo-change probability of the high-precision registration image and the structure registration uncertainty estimation to obtain a change reliability weight; under the constraint of the change reliability weight, performing consistency enhancement on the high-precision registration image to output a quantitative and reliable enhanced image. The application realizes high-precision, low-false alarm and quantifiable and reliable automatic registration and enhancement of multi-source large-scale satellite images.

A texture recognition device and a display device are provided. The texture recognition device includes a backlight element, configured to provide first backlight; a light constraint element, configured to perform a light divergence angle constraint process on the first backlight to obtain second backlight with a divergence angle within a preset angle range, the second backlight being transmitted to a detection object; and a photosensitive element, configured to detect the second backlight reflected by a texture of the detection object to recognize a texture image of the texture of the detection object.

The present application relates to the field of electronics. Provided in the embodiments of the present application are a detection assembly, a touch display screen, an electronic device and a control method therefor, which are used for providing a detection assembly integrating functions of bioelectric signal detection and identity recognition signal detection. The detection assembly comprises a texture recognition module, a first conductive layer and a bioelectric signal acquisition module. The first conductive layer is arranged on an acquisition side of the texture recognition module and serves as a biological sampling electrode. Fusing the texture recognition module and the first conductive layer serving as a biological sampling electrode with each other allows one detection assembly to have both functions of identity recognition and bioelectric signal acquisition. Thus, when users place parts to be tested such as hands on detection assemblies, identity recognition signals and bioelectric signals can be obtained.

The present application relates to the technical field of fabric texture detection, and particularly relates to a fabric defect detection method, comprising: collecting a fabric image; inputting spatial domain maps into spatial domain encoders of a ViT-S model respectively; inputting frequency domain maps into frequency domain encoders of the ViT-S model respectively; inputting a spatial domain first layer block vector, a spatial domain second layer block vector and a spatial domain third layer block vector into a TPP module; inputting a frequency domain first layer block vector, a frequency domain second layer block vector and a frequency domain third layer block vector into the TPP module; performing Concat operation on a fusion first layer feature, a fusion second layer feature, the spatial domain third layer block vector, the frequency domain first layer block vector, the frequency domain second layer block vector and the frequency domain third layer block vector, and then inputting the result into a classifier to output a classification result. The present application overcomes the problem that the recognition accuracy of traditional methods for complex fabric texture needs to be further improved.

The application discloses a control system of a six-degree-of-freedom hydraulic mechanical arm for dismounting and mounting a cylinder, relates to the technical field of cylinder dismounting and mounting, and is characterized in that a multi-angle polarized light source is configured at a light jump area on the surface of the cylinder, a brightness gradient sequence is established, a multi-exposure time chain is constructed, the light intensity is controlled in time, the reflection area is formed into a continuous light level, spot energy diffusion is performed to form a continuous texture transition zone, a pose feature track is extracted, a motion edge is generated by combining light intensity variation to identify an optical flow clue, a visual guide channel is constructed according to the clue, a hydraulic actuator is driven to perform pose fine adjustment, and synchronous compensation and pressure regulation of the alignment process are realized. The application realizes the continuous transition of the light on the surface of the cylinder through the multi-angle polarized light and the multi-exposure time chain, improves the imaging accuracy and the texture recognition stability, and realizes the pose fine adjustment and the pressure balance control by combining the optical flow guide, so that the hydraulic mechanical arm can complete adaptive assembly in a reflection interference environment, and the dismounting and mounting accuracy and safety are significantly improved.

The invention discloses a GPU context awareness-based code editor dynamic text rendering acceleration method, which comprises the following steps of: establishing a multi-dimensional semantic tag system comprising variables, functions, errors and structures, extracting semantic sub-dimensions of characters, generating character semantic tags through operation, caching the character semantic tags to a semantic tag SSBO, constructing a mapping rule of the semantic tags and rendering styles, and carrying out text rendering on the rendering styles according to the mapping rule. The CPU encodes the semantic rules into semantic rule textures; identifying structural units and layout attributes thereof, encoding the structural units into structural textures by the CPU, and uploading the structural textures to a structural layout SSBO of the GPU; the calculation shader reads two types of SSBO, respectively allocates four types of weights of semantics, interaction, structure and effect for characters, and generates a pattern weight texture after determining a fusion coefficient; the vertex shader calculates the final screen position of the character in parallel according to the structure unit to which the character belongs; and the fragment shader reads the two types of SSBO, each texture and the final screen position output by the vertex shader, hierarchical fusion is carried out according to a weight sequence, and character pixel-level rendering is completed.

This invention discloses a GPU-based context-aware code editor dynamic text rendering acceleration method. It establishes a multi-dimensional semantic tagging system including variables, functions, errors, and structures. Semantic sub-dimensions of characters are extracted and processed to generate character semantic tags, which are then cached in semantic tag SSBOs. A mapping rule between semantic tags and rendering styles is constructed, and the CPU encodes the semantic rules into semantic rule textures. Structural units and their layout attributes are identified, and the CPU encodes them into structural textures and uploads them to the GPU's structural layout SSBOs. The compute shader reads two types of SSBOs and assigns four weights—semantic, interactive, structural, and effect—to the characters. After determining the fusion coefficients, it generates style-weighted textures. The vertex shader calculates the final screen position of the character in parallel based on the structural unit to which the character belongs. The fragment shader reads the two types of SSBOs, each texture, and the final screen position output by the vertex shader, and fuses them layer by layer according to the weight order to complete pixel-level character rendering.

The invention discloses a wood texture identification method based on multi-scale feature fusion. The method comprises the following steps: firstly, extracting primary features of a wood texture image by using a ResNet50 backbone network; and then respectively inputting into a global branch and a local branch. The global branch performs down-sampling of different scales, then constructs a channel high-similarity attention mechanism under each scale, screens out low-correlation items in a channel similarity matrix through a dynamic threshold mechanism, performs weighted aggregation, performs residual connection with input primary features, and finally fuses the features of all scales, so as to obtain a channel high-similarity attention mechanism; and outputting the global features. The local branch calculates the spatial correlation between the positions of the pixel points in the primary features, screens out low correlation items through a dynamic threshold mechanism, and connects the low correlation items with input feature residuals after weighted aggregation to obtain local features. And finally, carrying out adaptive dynamic weighted fusion on the global features and the local features, accurately highlighting an area most related to classification, and remarkably improving the perception capability of the model for subtle differences.

The invention relates to a garbage cleaning method and system based on texture recognition and a medium. The method comprises the steps of obtaining a ground image of a target area; performing texture identification and image analysis on garbage in the ground image, and judging whether liquid garbage exists in the target area or not; determining a cleaning strategy of the target area according to whether liquid garbage exists in the target area or not; and cleaning the target area according to the cleaning strategy. On the basis of the short-wave infrared camera, the ground image can be collected in real time in the working process of the cleaning equipment, the cleaning strategy of the target area can be accurately determined and executed in combination with the texture recognition and image analysis technology, and compared with traditional intelligent cleaning equipment, the cleaning effect is better, the process is simple, and the execution efficiency is high.

The invention relates to a garbage cleaning method and system based on texture recognition and a medium. The method comprises the steps of obtaining a ground image of a target area; performing texture identification and image analysis on garbage in the ground image, and judging whether liquid garbage exists in the target area or not; determining a cleaning strategy of the target area according to whether liquid garbage exists in the target area or not; and cleaning the target area according to the cleaning strategy. On the basis of the short-wave infrared camera, the ground image can be collected in real time in the working process of the cleaning equipment, the cleaning strategy of the target area can be accurately determined and executed in combination with the texture recognition and image analysis technology, and compared with traditional intelligent cleaning equipment, the cleaning effect is better, the process is simple, and the execution efficiency is high.

The application discloses an aviation equipment detection and maintenance method and device based on target detection, and a medium, relates to the technical field of aviation equipment detection, and comprises the following steps: inputting a correction image set into a pre-trained target detection network, performing multi-scale feature analysis and edge texture recognition, and generating component detection results; establishing component topological relations according to the component detection results, and comparing the component topological relations with a standard configuration by using a graph structure analysis method to generate topological consistency information; performing defect positioning and quantitative analysis on the topological consistency information, extracting defect positions, defect types and defect quantitative indexes, tracking the expansion trajectories of the defect quantitative indexes with time through continuous time frame difference, and generating defect parameters; performing risk assessment on the defect parameters, generating a maintenance scheme, and executing the maintenance scheme, verifying the maintenance effect through real-time image detection, and forming a detection and maintenance closed loop. The application finally improves the accuracy and integrity of aviation equipment detection.

The invention discloses a wall aging damage intelligent diagnosis method and system based on an image recognition algorithm, and the method comprises the following steps: obtaining multi-source data of a building facade, the multi-source data comprising an unmanned plane oblique photography image, a handheld camera detail image, laser point cloud data and on-site environment metadata; time registration, space registration and standardization processing are carried out on the multi-source data, and a hierarchical labeling data set containing damage types and size information is constructed; according to the invention, through multi-source image fusion and standardization processing, the robustness of the model to multi-illumination, multi-climate and multi-material conditions is enhanced; unrelated areas such as vegetation, window frames and metal components are removed through intelligent extraction of wall body areas, and the recognition efficiency is improved; three-dimensional structure features are adopted to participate in identification decision, and the problem that a two-dimensional image cannot reflect deep damage is solved; through collaborative decision-making of the structure analysis model, the texture recognition model and the semantic segmentation model, the recognition capability of complex damage is improved.

The invention discloses a pork quality evaluation method and system based on texture recognition, and relates to the technical field of texture recognition analysis, and the method comprises the steps: marking pork, using a hyperspectral camera to shoot a pork multispectral image, and carrying out the preprocessing; constructing an image pyramid to perform image layering, and performing optimization processing on each layer of image based on the pixel relation of each layer of image to generate a multi-resolution image; re-fusing the multi-resolution images into an optimized multi-spectral image, and evaluating the pork quality based on the optimized multi-spectral image; the pork quality evaluation result is displayed and stored according to the mark number. According to the method, the intensity of detail information in the pork multispectral image is improved, help is provided for subsequent pork quality evaluation, the texture information is extracted and combined with the pixel state of the multispectral image to be input into the convolutional neural network for pork quality evaluation, and the accuracy and efficiency of evaluation are improved.

This invention provides a pattern and texture recognition method and apparatus based on neural networks, relating to the field of texture recognition technology. Specifically, it includes: constructing a texture recognition model; extracting multi-scale features from the input image using convolutional kernels of different sizes; weighting and fusing the features to generate a dynamically fused feature map; using a channel attention mechanism to extract global channel information and generate channel attention weights; utilizing a spatial attention mechanism, extracting spatial information and generating spatial attention weights by combining average pooling and max pooling of the feature map with a spatial feature interaction formula; fusing channel and spatial attention weights to weight the feature map; performing global average pooling on the optimized feature map to generate a global feature vector; and combining an improved regularized classification formula to generate the final classification result. This method achieves efficient pattern and texture recognition through multi-scale feature extraction, attention mechanism fusion, and regularized classification.

The invention discloses a coal sample bag real-time detection and violation replacement early warning method. The method comprises the steps of sample initialization and monitoring starting, sample circulation monitoring and violation early warning and authorized unsealing and monitoring termination. The coal sample bag is provided with a flexible electronic tag and an anti-counterfeiting texture; the radio frequency radar and the camera are deployed at key nodes of a circulation path; the reasoning server is in signal connection with the radio frequency radar and the camera and is used for operating a reasoning algorithm; the personnel identity verification device and the anti-counterfeiting texture recognition device are arranged at the unsealing use node; according to the invention, through three core modules of anti-counterfeiting mark enhancement, multi-dimensional state perception and intelligent risk research and judgment, the tamper-proofing and replacement-preventing capabilities in the sample circulation process are significantly improved.

A texture recognition module and a display device, the texture recognition module includes a texture recognition substrate including a texture acquisition region and a peripheral region at least partially surrounding the texture acquisition region, and the texture recognition substrate includes a base substrate, multiple photosensitive sub-pixels, an optical module layer and a support layer, the multiple photosensitive sub-pixels are arranged on the base substrate and located in the texture acquisition region, each photosensitive sub-pixel includes a photosensitive element and for texture acquisition, the optical module layer is arranged on a side of the multiple photosensitive sub-pixels away from the base substrate and configured to adjust light transmission, and the support layer is arranged on a side of the optical module layer away from the base substrate and includes an acquisition opening which exposes the multiple photosensitive sub-pixels. The texture recognition module has a better stability.

The invention discloses a control system of a six-degree-of-freedom hydraulic mechanical arm for disassembly and assembly of an oil cylinder, relates to the technical field of disassembly and assembly of the oil cylinder, and aims at an illumination jump area on the surface of the oil cylinder to configure a multi-angle polarized light source, establish a brightness gradient sequence and construct a multi-exposure time chain, and regulate and control illumination intensity in a time-sharing manner to enable a reflection area to form continuous illumination layers. Light spot energy diffusion is executed to form a continuous texture transition zone, a pose feature track is extracted and combined with light intensity change to recognize a motion edge to generate an optical flow clue, a visual guiding channel is constructed according to the clue, a hydraulic executing mechanism is driven to conduct fine adjustment on postures, and synchronous compensation and pressure adjustment in the alignment process are achieved. Continuous transition of oil cylinder surface illumination is achieved through multi-angle polarized light and multiple exposure time chains, and imaging precision and texture recognition stability are improved; and optical flow guide is combined to realize posture fine adjustment and pressure balance control, so that the hydraulic mechanical arm completes self-adaptive assembly in a reflection interference environment, and the disassembly and assembly precision and safety are remarkably improved.

The invention relates to a puncture guiding device and method, electronic equipment, a storage medium and a program product, and the device comprises an ultrasonic data collection module which is used for transmitting plane wave ultrasound with the frame rate higher than a frame rate threshold value to a muscle part and collecting echo signals to generate an ultrasonic image; the muscle texture recognition module is used for determining a scattering coefficient of a region in the ultrasonic image, dividing the ultrasonic image into a normal region and an abnormal region based on the scattering coefficient, and generating muscle texture information based on the normal region; the dangerous area identification module is used for constructing a dangerous area avoidance map based on the blood vessel identification result, the skeleton identification result and the nerve identification information; and the puncture guide line generation module is used for generating a puncture guide line based on the muscle texture information and the dangerous area avoidance map and displaying the puncture guide line. Under the condition that safety is ensured, objective and accurate recognition of lesion muscle textures is achieved, visual puncture guidance is provided, and accuracy, safety and the operation standardization degree are improved.

A texture recognition device and a display device are disclosed. The texture recognition device has multiple pixel units and includes a substrate, a driving circuit layer, a first electrode layer, and a photosensitive element layer. The driving circuit layer is disposed on the substrate, the first electrode layer is disposed on the side of the driving circuit layer away from the substrate, and the photosensitive element layer is disposed on the side of the first electrode layer away from the substrate. At least one of the multiple pixel units includes a pixel driving circuit disposed in the driving circuit layer, a first electrode disposed in the first electrode layer, and multiple photosensitive elements disposed in the photosensitive element layer that are spaced apart from each other. The pixel driving circuit is electrically connected to the first electrode, and the multiple photosensitive elements are disposed on the side of the first electrode away from the substrate and electrically connected to the pixel driving circuit through the first electrode. This texture recognition device has a better texture recognition effect.

The invention relates to the technical field of fabric texture detection, in particular to a fabric defect detection method which comprises the following steps: acquiring a fabric image; respectively inputting the airspace graphs into an airspace encoder of the ViT-S model; respectively inputting the frequency domain graphs into a frequency domain encoder of a ViT-S model; inputting the airspace first layer block vector, the airspace second layer block vector and the airspace third layer block vector into a TPP module; inputting the frequency domain first-layer block vector, the frequency domain second-layer block vector and the frequency domain third-layer block vector into a TPP module; and performing Concat operation on the fused first-layer feature, the fused second-layer feature, the space domain third-layer block vector, the frequency domain first-layer block vector, the frequency domain second-layer block vector and the frequency domain third-layer block vector, then inputting into a classifier, and outputting a classification result. The method solves the problem that the recognition accuracy of complex fabric textures needs to be further improved in a traditional method.

The present application relates to the technical field of image analysis, and particularly relates to a body-shaping garment production defect detection method and system based on image analysis. The method comprises collecting image data of a to-be-inspected body-shaping garment in a simulated wearing state in a current production batch; pre-processing the to-be-inspected image data to obtain pre-processed to-be-inspected image data; performing image texture recognition on the pre-processed to-be-inspected image data to obtain each texture geometric feature; obtaining a defect degree and a defect position of the to-be-inspected body-shaping garment according to each texture geometric feature and a preset geometric feature; and detecting production defects of the to-be-inspected body-shaping garment by using the defect degree and the defect position of the to-be-inspected body-shaping garment to obtain a defect detection result of the body-shaping garment. The present application aims to solve the problem that the existing inspection method is difficult to accurately detect defects of the to-be-inspected body-shaping garment, resulting in low detection efficiency and high missed detection rate of the to-be-inspected body-shaping garment.

This invention discloses an adaptive slicing device and its usage method based on the texture recognition of Codonopsis pilosula (Dang Shen) medicinal materials. It relates to the field of Codonopsis pilosula texture recognition technology and includes: a housing with a feed inlet on the side wall and a conveyor belt at the feed inlet; an image acquisition module including a near-infrared sensor and a high-resolution camera, the near-infrared sensor and the high-resolution camera facing the conveyor belt; a fixing module including a fixing seat, an elastic pressure plate, a guide, and two transverse grippers, with slide rails on both sides of the fixing seat, the guide positioned at the end of the conveyor belt near the fixing seat, and two transverse grippers; a slicing execution mechanism including a rotating blade and a servo motor, the rotating blade being rotatably mounted inside the housing via the servo motor, with multiple rotating blades evenly distributed around the output end of the servo motor; and a collection trough located below the fixing seat. This invention achieves automated multimodal real-time recognition through the image acquisition module and adaptively fixes the medicinal material through the fixing module, reducing damage to the medicinal material.

The invention provides a surface rock classification method and system based on deep learning, and the method comprises the steps: obtaining a rock in-situ image and spectral data from multiple sensors, and obtaining a preliminary multi-modal representation through fusing a physical shape and surface texture distribution; processing the preliminary multi-modal representation by adopting a multi-modal fusion algorithm, and determining a unified spatial-temporal feature vector in combination with mineral reflection characteristics and weathering degrees; analyzing and correcting the feature set through a convolutional neural network, and obtaining spatial distribution details from the three-dimensional contour and the internal fracture to judge the potential rock category probability; aiming at the correlation between the spatial distribution details and the in-situ traces, enhancing texture recognition under an illumination condition by adopting an attention mechanism to obtain an enhanced feature map; determining the influence degree of the weathering time sequence change from the enhanced feature map, and if the influence degree is lower than a preset threshold value, integrating the spectral characteristics to generate a final classification label; and according to comparison between the final classification label and the depth feature, whether a preset consistency standard is met is judged, and high-precision rock type output is obtained.

The application discloses a kind of sensing fabric surface texture identification methods based on intelligent sensor, it is related to texture identification technical field, comprising the following steps: S1, output triboelectric signal sequence;S2, output texture friction high-frequency signal sequence;S3, construct standardization microtexture feature vector;S4, utilize improved DeepWalk model to combine anisotropic diffusion kernel simulation manifold diffusion process, by tensor ring decomposition and tangent space alignment aggregation mapping is low-dimensional embedding, based on tangent space relative geometric distance executes classification decision, output initial texture identification result;S5, output logic optimal texture category;S6, output final texture identification result;S7, the consistency of identification result and the surface to be measured is verified by tactile interaction.The application overcomes the limitation that prior art ignores contact mechanism, lacks essential description and lacks verification means, significantly improves the robustness and accuracy of texture identification.

A flatness detection method and system of a paint-sprayed aluminum plate, relating to the field of metallic property detection. The method is applied to an aluminum plate detection system, and the method comprises: obtaining a first surface image of the aluminum plate captured by an optical camera, the first surface image being composed of a plurality of pixel points, and each pixel point storing an RGB value and a brightness value; performing anti-reflection suppression processing on the first surface image to obtain a second surface image; performing image enhancement on the second surface image to obtain a third surface image; and performing texture recognition on the third surface image to obtain the flatness of the aluminum plate. The technical solution provided in the application solves the problem of low recognition accuracy of the flatness of the aluminum plate surface caused by the reflective characteristic of the aluminum plate material.