228 results about "Corner detection" patented technology

The invention discloses an aluminum profile surface defect automatic detection method and system based on image recognition, and relates to the technical field of image processing and intelligent detection.The method comprises the steps that three-dimensional geometric data of the section of an aluminum profile are obtained through a three-dimensional scanning device, candidate observation angles and geometric feature parameters needed by coverage calculation are extracted, and the three-dimensional geometric data of the section of the aluminum profile are obtained; if the section shape contains a groove or a curved surface structure, marking a space coordinate range corresponding to the area of the shadow region to obtain a section geometric feature vector and a shadow region coordinate set; according to the aluminum profile surface defect automatic detection method and system based on image recognition, all-dimensional dead-corner-free detection of the surface of the aluminum profile is achieved, the method and system can adapt to the production takt of complex section shapes and changes, the accuracy and comprehensiveness of defect detection are improved, and effective technical support is provided for aluminum profile quality control.

The invention discloses a two-stage infrared and visible light image registration method, system and device. The method comprises the following steps of image preprocessing, contour extraction, angular point detection, feature description and matching, affine transformation estimation, multi-scale optical flow estimation, optical flow constraint and loss function, reverse resampling and fusion and error evaluation. According to the invention, rough registration is carried out by using contour angular point features, so that a preliminary alignment result can be quickly obtained; refined alignment is carried out in combination with an unsupervised optical flow network, and sub-pixel-level registration precision is achieved. The contour angular points are based on shape information of an image target, are natural and are not influenced by spectral differences, and the matching stability is enhanced through main direction and angle features. The unsupervised depth optical flow model estimates a pixel displacement field by learning consistency characteristics of an input image, and does not need to depend on annotation data. The combination can effectively eliminate the difference between infrared light and visible light, and improves the robustness and adaptability of registration.

The present invention relates to the field of rock structure measurement, and disclosed are a three-dimensional pore reconstruction method and system based on a rock image, for use in solving the problem that using a machine learning method to reconstruct pores in a rock image requires relatively high mathematical or computer science expertise, involves high labor and economic costs, and is difficult to apply to small-scale rock pore reconstruction projects. The method comprises: step 1: grouping slice images of each rock sample into one set, and performing preprocessing; step 2: performing grayscale processing, histogram equalization and normalization processing on an image; step 3: using a Harris corner detection algorithm to perform corner detection, sorting Harris response values, selecting key points by setting the number of key points, and using a non-maximum suppression method to screen the selected key points; step 4: segmenting the boundaries of pores in the image by means of an adaptive threshold selection method, and extracting pore features; and step 5: using a marching cubes algorithm to construct a three-dimensional pore reconstruction model. The present invention is used for three-dimensional reconstruction of pores in a rock image.

The invention discloses an image processing method and device, equipment and a storage medium, and is applied to the technical field of image processing. According to the scheme, the method comprises the following steps: detecting angular points in a target image to be interpolated based on an angular point detection algorithm to obtain feature points in the target image; for the texture complexity of the neighborhood image of each feature point, selecting a target interpolation algorithm for processing the feature point; and performing interpolation processing on the feature points based on a target interpolation algorithm to generate an interpolation result image. For angular points with complex textures, a high-precision interpolation algorithm is adopted to ensure that image details are finely restored; and for the angular points with slightly simple textures, a low-calculation-amount algorithm is used to reduce resource occupation, so that the limitation that a single algorithm is uniformly adopted for the whole image in the prior art is avoided, and the redundant calculation amount is remarkably reduced while the interpolation precision of the key feature region is ensured and the quality of the whole image is maintained.

The embodiment of the invention provides a pose estimation method and device and electronic equipment, and belongs to the technical field of visual inspection and positioning. The method comprises the following steps: acquiring a target image in real time, wherein the target image is an image of a target object containing a similar two-dimensional code ARuco; performing image preprocessing according to the target image to obtain binary image data; performing mark detection processing according to the binarized image data and a pre-established multi-scale image pyramid so as to identify and position a similar two-dimensional code ARuco mark; performing camera calibration processing according to preset camera parameters to obtain camera calibration data; performing corner detection processing according to the ARuco mark to obtain positioning corner position information; and according to the positioning corner point position information, the preset target three-dimensional space position information and the camera calibration data, carrying out mark attitude calculation processing to obtain target pose estimation information. The method is used for achieving the effect of improving the accuracy of the pose estimation result.

The invention relates to the technical field of camera testing, in particular to a camera triangulation distance measurement detection method. Comprising the following steps: selecting a tested camera, and adjusting the position and angle of the camera; according to the positions of the two targets in the image, setting test areas corresponding to different view field angle pictures, and adopting an area detection mode; angular point detection is carried out on the set area, the angular points and sub-pixel-level angular points are obtained, and checkerboard center angular points are judged and corrected; according to a distortion calibration model, selecting a corresponding function to carry out distortion removal calculation on the angular point to obtain a normalized distortion-removed angular point coordinate, and converting the normalized distortion-removed angular point coordinate into an actual distortion-removed pixel coordinate; calculating the calculation distance of two target angular points of each image, establishing a normalized three-dimensional coordinate, calculating the deviation angle of the left and right angular points by using the three points, and combining the measured parameters to obtain the calculation distance of the two angular points; and calculating a precision error. According to the technical scheme, the accuracy of the internal reference calibration data and the reliability of the distortion model can be effectively verified.

The invention discloses a house type recognition and three-dimensional reconstruction method and system based on grating images, and belongs to the technical field of house type image recognition. The method comprises the steps of 1, collecting and screening high-quality house type images suitable for national conditions of China regions, and constructing a grating house type image vector data set; 2, identifying walls, doors, windows and scales in the house type image based on a key point detection network, and generating and screening candidate primitives through an axial alignment rule and a constraint condition; step 3, detecting and positioning the endpoints of the scale by using a mode of combining Yolov8 and Shii-Tomasi angular point detection, and performing scale digital character recognition through a pre-trained multi-mode optical character recognition model OFA-OCR (Orthogonal Frequency Amplification-Optical Character Recognition); and step 4, designing and realizing a house type identification and three-dimensional reconstruction system based on a WebGL framework, and supporting rendering of reconstructed wall and door and window views, interaction of a user to the reconstructed house type, and forward and backward functions of house type operation historical records.

The invention relates to an X corner detection method based on multilevel matching and feature optimization, and belongs to the technical field of computer vision and image processing. The method comprises the following steps: making a high-precision template corresponding to a predetermined X corner mark, so that the high-precision template can be accurately focused on a corner area; carrying out noise reduction and filtering operations on the image before identifying the mark so as to remove noise interference; carrying out template matching on the preprocessed image by adopting a self-adaptive threshold value and a multi-scale search strategy; and performing multi-level detection on the basis of template matching, including ORB feature extraction, Harris corner optimization and sub-pixel level positioning, and outputting the accurate position of the corner and a feature descriptor. The method can still maintain high precision and stability in a complex scene, is suitable for the fields of visual positioning, three-dimensional reconstruction and the like, and has the advantages of high detection efficiency, strong anti-interference capability, wide applicability and the like.

The invention relates to the field of bottle cap visual inspection, in particular to an AI-based bottle cap 360-degree full-view-angle defect high-speed detection method and system, the method comprises a detection image acquisition step, an image processing and splicing step, a defect identification detection step and a detection result verification step, through multi-sensor collaborative layout, 360-degree dead-corner-free detection of a bottle cap is achieved, and the detection accuracy is improved. And comprehensive detection of the bottle cap is ensured through a high-speed detection mechanism, a clustering model is constructed by utilizing historical defect data, abnormal defects are judged through mahalanobis distance, and the model is dynamically updated based on transfer learning, so that rapid learning of novel defects is realized, and the manual intervention cost is reduced.

The invention discloses a coal cutter roller cutting interference state identification method based on an infrared image, and the method employs an infrared camera to shoot the surrounding environment, effectively prevents the recognition precision from being limited by complex illumination environments, such as underground low illumination, high dust, and strong water mist, and guarantees the stability and definition of a target contour. Then analyzing and processing the infrared image through a deep learning network, determining four corner points of the hydraulic support face guard in the area from the infrared image by adopting a Shii-Tomasi corner point detection algorithm, further accurately calculating the spatial attitude of the hydraulic support face guard, and constructing a three-dimensional trajectory model of the coal mining machine roller; the Kalman filtering is adopted to obtain the predicted poses of the two, and then the cutting interference state is judged through early warning grading, so that the response speed and the safety guarantee capability of the fully-mechanized coal mining system are remarkably improved, the dynamic prediction and real-time response of the cutting interference state are realized, and the efficiency and the precision of cutting interference state recognition are ensured.

The invention discloses a quality detection device for electric lifter processing, and belongs to the technical field of quality detection.The quality detection device comprises a device base, the two sides of the top of the device base are each slidably provided with a sliding frame, the two sliding frames are each in a U shape, and the middle sections of the tops of the two sliding frames are each fixedly provided with a supporting frame; first hydraulic telescopic rods are fixedly arranged on the two sides of the tops of the two supporting frames, first telescopic heads are arranged on the tops of the four first hydraulic telescopic rods in a telescopic mode, and a mounting frame is fixedly arranged between the first telescopic heads arranged on the first hydraulic telescopic rods located on the top of the same supporting frame. And the comprehensiveness and the accuracy of the detection are obviously improved. The system can automatically switch middle, local and corner detection modes by means of a multi-station detection process engine, and realizes 360-degree omnibearing detection of the electric lifter main body in cooperation with the rotation function of the detection placement table and the transverse movement of the sliding frame, thereby avoiding a detection blind area.

The invention relates to the technical field of ultrasonic medical robots, and discloses a superficial ultrasonic scanning path planning method and device based on an SVD-improved RRT algorithm. The superficial ultrasonic scanning path planning method is applied to ultrasonic medical robot equipment, and specifically comprises the following steps: S101, receiving a data acquisition request sent by a main control end, controlling a Kinect camera to acquire a superficial depth image, processing 20 groups of calibration board images through a Matlab calibration tool box, and executing a corner detection algorithm to identify checkerboard corners, and optimizing and solving the internal reference matrix of the infrared camera. According to the method, SVD plane fitting and an improved RRT algorithm are fused, the precision and efficiency of an ultrasonic scanning path are remarkably improved, in the point cloud processing stage, statistical filtering and voxel gravity center down-sampling synergistically act, point cloud noise is reduced by 90%, meanwhile, more than 95% of three-dimensional features are reserved, a high-fidelity data basis is provided for path planning, and the method has good application prospects. SVD plane fitting forcibly passes through the center of mass, and the error is smaller than or equal to 0.5 mm

The invention provides a centralized multi-robot collaborative SLAM (Simultaneous Localization and Mapping) method based on an FPGA (Field Programmable Gate Array) platform. The method comprises the following steps: acquiring a color image and IMU (Inertial Measurement Unit) data of a surrounding environment by using a motion camera The color image is input into an FPGA platform of the single robot and converted into a grey-scale map, image pyramid scaling, corner detection and feature description are carried out, and the image is transmitted to a processing system of the single robot; the processing system generates observation information and further generates an image frame; generating a key frame and sending the key frame to a central server; the central server side carries out loopback detection on the key frame, carries out map fusion on local maps established by different single robots to obtain a global map, and optimizes the global map; and constructing a three-dimensional dense point cloud map. According to the method, cooperative positioning and three-dimensional mapping of multiple robots can be carried out in a large-scale and complex environment scene, the problem that computing resources and a detectable range of a single robot in the complex scene are limited is solved, and the scale, the precision and the robustness of map construction are improved.

The invention discloses a target ground object AI identification method based on a multi-source aerial photo, and relates to the technical field of aerial photo processing, and the method comprises the following steps: S1, collecting a panoramic aerial photo of a to-be-identified region; s2, extracting a first data set by using the gray-level co-occurrence matrix of the panoramic aerial photo; s3, extracting a second data set according to response values of all angular points in the panoramic aerial photo; s4, inputting the first data set and the second data set into a preliminary screening condition model, and taking pixel points meeting the preliminary screening condition model as suspected target ground objects of the to-be-identified region; and S5, processing the suspected target ground feature of the to-be-identified area to obtain an accurate target ground feature. In order to overcome the limitation of a single feature, multiple information sources, such as texture features extracted by a gray-level co-occurrence matrix and structural features extracted by corner detection, are fused, the features of image control points in aerial photography can be described more comprehensively, and the accuracy and robustness of recognition are improved.

The invention relates to the technical field of image processing, in particular to an image data acquisition method and system for a lens plastic part, and the method comprises the steps: obtaining and preprocessing a multi-view surface image, and then evaluating the possibility of a light reflection region according to the gray value and texture features of a pixel point; calculating an angular point response function of each pixel point according to a Harris angular point detection algorithm, presetting a local window size for the pixel points, and correcting the angular point response functions according to the contrast ratio of the pixel points in the local window and the possibility of a reflective area; feature points are obtained according to the corrected angular point response function, multi-view-angle image registration is carried out, the registered multi-view-angle images are fused, a complete image of the surface of the lens plastic part is obtained, and data of the complete image are conveniently stored as a final collection result. According to the method, the problem of analysis difficulty caused by light reflection and feature deficiency is analyzed, so that high-quality acquisition of the full-coverage image on the surface of the lens plastic part is realized.

The invention relates to the technical field of image processing, in particular to an online calibration method and system for hydrological flow data, and solves the technical problem of low flow calculation precision caused by rough window segmentation and illumination interference in the prior art. The method comprises the following steps: collecting a water surface video in a river water flow process, and setting a velocity measurement line area in the water surface video along a water flow direction; according to the gray level distribution of the velocity measurement line area, carrying out illumination state judgment, and according to a judgment result, carrying out image restoration on an illumination abnormal area in the water surface video to obtain a restored water surface video; constructing a space-time image for the repaired water surface video, performing corner detection, clustering corners to divide a plurality of windows, and constructing a window weight for each window; and weighting the texture angle of each window according to the window weight of each window to obtain a weighted texture angle, and calculating hydrological flow data according to the weighted texture angle.

The invention discloses a robot multi-component low-cost high-precision calibration method based on a consumer-level camera, and the method comprises the steps: photographing a calibration plate through the consumer-level camera, and optimizing the internal parameters of the camera through corner detection and nonlinear optimization; a camera is installed at the tail end of the mechanical arm, about 50 sets of calibration plate images are shot, the pose is obtained by means of forward kinematics of the mechanical arm, and the pose relation between the camera and the mechanical arm is analyzed through the formula AX = XB; then the chassis and the camera on the mechanical arm are used for joint calibration, the calibration relation between the mechanical arm and the trolley is solved by moving the trolley and the mechanical arm observation calibration plate, and finally the calibration precision is verified by calculating the re-projection error. The method does not need to depend on high-precision equipment and a professional calibration field, calibration can be completed by using a common consumer camera and a simple calibration board, the method can adapt to various cameras, the requirement for computing equipment is not high, and the requirement can be met by a common workstation. The method can be carried out in a dynamic scene, the calibration time is within 1 hour, and the precision is within 5 mm.

The invention provides a dense point cloud-based building feature line extraction method, and belongs to the technical field of building feature line extraction, and the method comprises the following steps: carrying out the data preprocessing of point cloud data, carrying out the edge detection and corner detection of the preprocessed point cloud data, and carrying out the linear segment detection and curve segment detection of feature detection data, and performing line segment connection and removal processing on the line segment detection data, establishing a mapping relation table of optimized data, performing three-dimensional data model reconstruction on the building, and generating a three-dimensional model of the building. The cloud point data is added possibly at the beginning, errors and defects caused by environmental influence can be avoided, raindrop data are richer and more perfect, then the cloud point data are denoised, summary recognition of an initial model is achieved, irrelevant points are removed in the later period, follow-up data processing is more accurate, and the accuracy of raindrop data processing is improved. And through edge and curve identification, the boundary of the model can be better planned, and the subsequent processing efficiency is higher.

The invention provides a corner detection method and device, computer equipment and a program product, and the method comprises the steps: detecting each corner in a checkerboard image through employing a pre-trained corner detection network, and obtaining a first detection frame corresponding to each corner; performing image optimization operation on the detection frame images corresponding to the first detection frames to obtain optimized images; according to the gradient of each pixel point in the optimized image, screening out candidate saddle points conforming to saddle point features from each pixel point; and according to the first coordinate of each candidate saddle point in the optimized image, screening out a target corner point in the first detection frame from each candidate saddle point.

The invention relates to the technical field of image processing, in particular to a shaft part forging forming monitoring method and system. The method comprises the following steps: acquiring characteristic values of pixel points in a surface image of a formed shaft part in a plurality of channels in an HSV color space, and gradient direction angles of the pixel points; determining the local consistency of the pixel points; determining a weighted Euclidean distance between the pixel points; clustering all pixel points according to the weighted Euclidean distance to obtain a plurality of clusters; determining the gradient saliency and the number of candidate angular points of the image region where the clustering cluster is located; and on the basis of the number of the candidate corner points, the candidate corner points are obtained through a Harris corner point detection algorithm, and whether forging of the shaft part is qualified or not is measured according to the Euclidean distance between the candidate corner points. According to the method, the problem of inaccurate recognition caused by large feature difference of different geometric regions is solved through the number of the candidate angular points of the image region where the adaptive clustering cluster is located, and the accuracy of monitoring the forging quality of the shaft parts is improved.

The invention discloses an acupoint recognition and positioning method and system, a moxibustion robot and a storage medium. The method comprises the steps of obtaining multi-modal data of a human body to be subjected to physiotherapy; carrying out preprocessing on the multi-modal data; constructing a multi-modal feature fusion model, and generating an enhanced acupoint feature map; constructing a human body meridian-acupoint incidence relation matrix on the basis of the enhanced acupoint feature map in combination with a preset human body meridian-acupoint incidence relation matrix; dynamically compensating the candidate area, calculating the real-time offset of the acupuncture point based on the body surface micro-motion data, and correcting the depth coordinate of the acupuncture point in combination with the body surface elastic modulus data to obtain the accurate three-dimensional coordinate of the acupuncture point to be positioned; on the basis of the precise three-dimensional coordinates, blood vessels and nerve dense areas around the acupuncture points are recognized through an improved Harris corner detection algorithm, and a safe moxibustion applying path is generated; meanwhile, the confidence value of the precise three-dimensional coordinate is calculated. According to the method, high-precision, self-adaptive, all-position compatible, safe and controllable acupuncture point recognition positioning is realized.

The invention discloses a camera calibration method and device, equipment and a storage medium. The method comprises the following steps: acquiring a first image acquired by shooting a calibration plate by a camera; wherein the calibration plate comprises a plurality of angular points; performing inverse perspective transformation on the first image based on the internal reference and the initial external reference of the camera to obtain a second image; detecting angular points in the second image to obtain a first angular point detection result; based on the first corner detection result, the first image and the internal reference, solving to obtain a target external reference of the camera; and calculating an error parameter based on the target external parameter, the internal parameter and the first image, and when the error parameter satisfies a preset condition, determining that camera calibration is completed. According to the technical scheme of the invention, the inverse perspective transformation is carried out on the first image shot and collected by the calibration plate, so that the influence of partial camera distortion can be eliminated, the accuracy of angular point detection is improved, and the success rate and accuracy of camera calibration can be improved.

The invention relates to the field of image processing, in particular to a surface crack intelligent identification method in a radiator vibration test, and the method comprises the steps: collecting a radiator surface image in real time, and carrying out the preprocessing of the image, and obtaining a radiator gray-scale map; calculating the smoothness of each pixel point of the grey-scale map, the key point density in the neighborhood and the edge degree, and taking the product of the smoothness, the key point density and the edge degree as the optimal degree of the pixel point; obtaining a self-adaptive angular point threshold based on the optimization degree, and carrying out angular point detection on the gray-scale map according to the self-adaptive angular point threshold to obtain all key points through detection; and based on the detected key points, utilizing an LKT optical flow method to realize crack detection on the surface cracks of the radiator. According to the self-adaptive angular point threshold value, the sufficient number of uniformly distributed angular points can be obtained in different areas of the radiator, so that the accuracy and robustness of crack detection are improved.

The invention relates to a camera lens focusing definition analysis method and system, and the method comprises the steps: obtaining a checkerboard image collected by a to-be-tested camera lens module; performing angular point detection on the checkerboard image to obtain angular points of checkerboards in the checkerboard image; determining a test area from the checkerboard image based on the angular points of the checkerboard, and calculating a definition index of the test area; and performing subdivision statistics on the definition index of each test area based on the sagittal direction and the meridian direction of the camera lens to obtain the definition index of the camera lens module to be tested after subdivision statistics. According to the invention, the method achieves the automatic determination of the test region based on corner detection, can efficiently obtain the definition index, achieves the subdivision and statistics of the indexes in the sagittal direction and the meridian direction of the camera lens, can achieve the complete definition evaluation of the whole imaging view, helps a camera production process to improve the efficiency, and improves the production efficiency. The problem of low efficiency in existing camera focusing debugging is solved.

The invention discloses visual detection equipment for surface defects of a silicon wafer, and relates to the technical field of semiconductor detection, and the technical key points are that the visual detection equipment comprises a base and a bracket fixedly mounted on the back surface of the base, a detector positioned right above the base is fixedly mounted on the bracket, and a position adjusting mechanism is arranged at the top of the base; a quantum dot fluorescence mechanism is arranged on the front face of the base, the position adjusting mechanism comprises a three-dimensional position adjusting assembly and a containing table, and the three-dimensional position adjusting assembly comprises an X rod fixedly installed on the top of the base. Free movement, accurate positioning and quantum dot fluorescence enhancement-based defect detection of the silicon wafer in a three-dimensional space are realized, the limitation of traditional equipment is broken through, wafers with different sizes and shapes are flexibly coped, comprehensive coverage and dead-corner-free detection are ensured, meanwhile, the accuracy and sensitivity of detection are remarkably improved, and the detection efficiency is improved. The detection error and the omission ratio are reduced, and reliable quality guarantee is provided for the semiconductor manufacturing industry.

The invention discloses a security and protection monitoring big data processing system based on cloud computing, and the system comprises a data receiving module which is used for receiving security and protection monitoring big data which comprises video data; relates to the technical field of data processing, through the arrangement of a target picture frame extraction module, video data are not directly transmitted, stored and processed after the video data are received, a target picture frame is extracted from mass video data, a specific area is determined by using a corner detection algorithm, and a target picture frame is obtained. A to-be-deleted picture group is divided through comparison values generated by comparing a specific area through a Bhattacharyya distance, a Hamming distance and the like, a large number of repeated or similar picture frames in the video data are deleted, then the target picture frame group is transmitted, stored and processed subsequently, the data volume of subsequent processing is greatly reduced for the video data, and the video data processing efficiency is improved. Calculation time and storage resources are effectively saved, and the overall processing efficiency of the video data is improved.

This invention discloses an automated anterior tooth feature parameter extraction method and evaluation system based on computer vision, comprising: acquiring and visualizing a full-mouth scan image of a patient to be processed; cropping the corresponding tooth portion image; using Gaussian blur to eliminate specular reflection; using color segmentation to set thresholds for the H, S, and V channels respectively; separating the teeth and gingiva in each tooth portion image and performing morphological processing on each; extracting the tooth and gingiva portion contours using contour extraction methods; further refining the segmentation accuracy using the Chan-Vese active contour model algorithm and the energy function within the tooth portion contour; extracting corner points using the Shi-Tomasi corner detection method and then clustering them; extracting other important evaluation feature points through curve fitting and extreme value finding methods; and finally calculating relevant objective indicators for tooth evaluation and automatically generating the corresponding tooth score, thus completing the automated anterior tooth evaluation.

The invention relates to the technical field of industrial detection, in particular to a pin defect detection method, device and equipment and a medium. According to the method, based on pin target positioning, image segmentation is carried out on a pin image, form extraction is carried out on a pin area, an actual included angle between a central axis and a reference axis is calculated, whether the actual included angle is in a preset angle range is detected, and if the actual included angle is detected not in the preset angle range, the pin area is determined. And if so, determining that the corresponding pin has a bending defect. The method comprises the following steps: acquiring a pin image from an original image based on pin target positioning, segmenting to obtain a pin area, extracting morphological characteristics of the pin area to determine a central axis, determining a reference axis according to the pin image, and calculating an actual included angle between the reference axis and the pin image; and finally, determining whether the pin has a bending defect or not by judging whether the included angle is within a preset range or not. And performing morphological analysis on the pin image to determine an included angle between the central axis and the reference axis so as to complete defect detection of the pin.

The utility model belongs to the technical field of detection rulers, and particularly discloses a pressure vessel corner angle detection ruler. Comprising a main ruler and an auxiliary ruler which are rotationally connected, indicating mechanisms and level bubbles are arranged on the main ruler and the auxiliary ruler, the indicating mechanisms are located at the free ends of the main ruler and the auxiliary ruler, and a detection mechanism is movably arranged on the front side of the main ruler. The main ruler and the auxiliary ruler are rotationally connected, so that the total length of the detection ruler is flexibly adjusted, the auxiliary ruler can rotate around the rotating shaft relative to the main ruler, integrated detection of multiple parameters such as length, straightness, curvature, height and width can be realized, and the detection efficiency is greatly improved. The problem that the existing corner angle detection technology is difficult to adapt to the actual requirements of portability, instantaneity, manual operation and direct reading of specific deviation values in the manufacturing, mounting and inspection field of the pressure vessel is solved.

The invention relates to the technical field of sorting and recognition, and particularly discloses an intelligent recognition and sorting device and method for defective cans, the device comprises a detection and recognition unit, the detection and recognition unit comprises a machine body, a carrying mechanism is arranged in the machine body, and the carrying mechanism is used for transferring cans entering the machine body from one side of the machine body to the other side of the machine body; the two ends of the carrying mechanism correspond to the conveying line and are arranged close to the conveying line, a plurality of visual detection mechanisms are further arranged on the two sides of the carrying mechanism, and the visual detection mechanisms are used for detecting and identifying whether the cans transferred by the carrying mechanism have flaws or not; the carrying mechanism comprises a transfer assembly and a plurality of placing stations, the placing stations are arranged outside the transfer assembly in an array mode and are in running fit with the transfer assembly, when the transfer assembly works, the placing stations rotate along with the transfer assembly and achieve autorotation of the cans, then 360-degree dead-corner-free detection of the cans is achieved, and therefore the recognition and sorting precision and efficiency of the defective cans are improved.