48 results about "Subpixel edge detection" patented technology

The invention discloses an intelligent positioning method and system for can making stamping, and relates to the technical field of image processing. Comprising the following steps: acquiring a tank stamping part image, and segmenting the tank stamping part from a background by adopting a self-adaptive threshold segmentation algorithm to obtain segmented tank region image data; utilizing a moment-based sub-pixel edge detection method to accurately position the edge coordinates of the contour of the tank body, and obtaining the key dimension parameters of the tank body; extracting texture features of the surface of the tank body by adopting a gray-level co-occurrence matrix and a local binary pattern algorithm, and judging whether the texture features belong to surface defects or not through a support vector machine classification model; the method solves the problem that edge information is difficult to accurately extract when a traditional image processing method faces tank surface reflection and a complex background, ensures the accuracy of a measurement result by establishing a tank coordinate system and performing attitude correction, overcomes the problem that the tank is easy to deviate or incline in the production process, and improves the production efficiency. And high-efficiency and accurate quality detection of the tank stamping part is realized.

The invention discloses a building structure crack intelligent detection method, which comprises the steps of S1, constructing a multi-dimensional intelligent sensing array, and obtaining four-dimensional spatio-temporal data including vision, stress, vibration and temperature; s2, performing feature enhancement processing on the multi-modal data; s3, realizing cross-modal crack identification and positioning based on a space-time attention neural network; s4, adopting a sub-pixel edge detection and ultrasonic genetic inversion algorithm; s5, constructing a coupling dynamics prediction model; s6, establishing a dynamic threshold evaluation and multi-dimensional self-calibration mechanism; 0.05 mm micro-crack identification and 3mm depth detection precision are achieved and are improved by 50% compared with a traditional method, the detection accuracy under the complex environment is larger than or equal to 98% through the multi-modal data fusion and GAN enhancement technology, the RMSE is smaller than or equal to 0.03 mm through short-term prediction, the accuracy is larger than or equal to 85% through long-term prediction, double guarantees of a physical mechanism and data driving are established, and the detection accuracy is improved by more than or equal to 98% through the multi-modal data fusion and GAN enhancement technology. And the system has self-calibration, self-adaptive sampling and multi-modal fusion decision-making capabilities, so that the manual intervention cost is greatly reduced.

The invention discloses an automatic instrument reading method and system based on embedded machine vision. The method comprises the following steps: deploying an embedded image acquisition unit with wide dynamic imaging capability; performing multi-mode preprocessing of white balance correction, contrast enhancement and noise suppression on the image; self-adaptively identifying the instrument type and positioning a key area by using a lightweight convolutional neural network; hough transform and sub-pixel edge detection are adopted for a pointer type instrument to achieve high-precision angle analysis, and optical character recognition is completed for a digital instrument through a bidirectional long-short-term memory network; and finally, fusing multi-frame results through Kalman filtering, evaluating credibility, and outputting stable readings with timestamps. According to the technical scheme, high-robustness, high-precision and low-delay automatic reading can be realized at the embedded end, and the operation stability and the long-term adaptability of the system in a weak network environment are remarkably improved.

The invention relates to the technical field of vertical shaft guide detection, in particular to an industrial intelligent visual detection and diagnosis system for a vertical shaft guide. The coal dust shadow quantization unit extracts a coal dust region by adopting a double-peak self-adaptive threshold segmentation combined region growing algorithm, and introduces a space weight factor to calculate an average gray value and a coverage area proportion, and the dynamic coupling calibration unit depends on a three-dimensional lookup table calibrated by multiple working conditions and nonlinear interpolation; the optimal deviation compensation coefficient of the jitter and coal dust combination is matched, a dynamic gray segmentation threshold value is generated, and a defect diagnosis decision unit accurately calculates the abrasion depth through partition self-adaptive segmentation, multi-scale morphological filtering and denoising and gradient amplitude and curvature double-constrained sub-pixel edge detection. A hierarchical control instruction is output according to the real-time depth and the historical trend, coupling interference is eliminated, the cage guide abrasion detection accuracy is improved, and a reliable basis is provided for safe operation and maintenance.

This invention provides a machine vision-based method and system for measuring the dimensions of circular threads. First, it achieves complete coverage of the entire thread profile by acquiring multiple local images, overcoming the limitation of a single field of view in capturing the entire thread. Second, it extracts the contours of each image frame through sub-pixel edge detection and performs in-depth analysis of feature points within each frame. Subsequently, a feature-constrained stitching model is established to achieve precise alignment and global optimization of data from different fields of view, reducing the error amplification effect caused by geometric mismatch and viewing angle deviation, and suppressing nonlinear errors in parameter accuracy within the measurement range. This method can comprehensively and accurately measure key parameters of circular threads (such as taper, pitch, thread profile variation, and minimum effective connection length). This method not only significantly improves the overall accuracy and reliability of thread measurement but also provides a solid technical guarantee for the safety and durability of oil well pipeline connections, and has important reference value for research and application in related fields.

The invention provides a CT (Computed Tomography) system defect detection capability reference block, evaluation method and system. The CT system defect detection capability reference block comprises a defect simulation sample A and a matrix disc B, the method comprises the following steps: preprocessing a CT detection image, extracting a defect region of interest, and obtaining a defect detection capability evaluation two-dimensional plane; correcting the voxel size of the CT image; carrying out sub-pixel edge detection; fitting a result, and establishing an image pixel position coordinate; according to the sub-pixel edge point set, matrix gray scale mu l (h) and defect gray scale mu b (h) at different positions are obtained; establishing a relational expression of the defect size and the gray level, drawing a relation curve I of the defect size D and # imgabs0 # and a human eye 50% probability recognition probability curve II, and obtaining a minimum recognizable defect; and establishing a relation curve of the defect size and the measurement error thereof. According to the method, the defect size measurement precision is high, the influence of image noise is small, and the minimum defect detection capability and defect measurement error can be quantitatively evaluated.

According to the machine vision-based round thread size measurement method and system provided by the invention, a plurality of local images are collected to realize complete coverage of a whole thread profile, so that the limitation that the whole thread view is difficult to capture in a single view field is overcome; secondly, sub-pixel edge detection is carried out, the outline of each mapsheet is extracted, and deep analysis is carried out on feature points in each mapsheet; then, a splicing model based on feature constraint is established, accurate alignment and global optimization of different view field data are realized, an error amplification effect caused by geometric mismatch and view angle deviation is reduced, and nonlinear error suppression of parameter precision in a measurement range is realized. According to the method, the key parameters (such as taper, thread pitch, thread form change and minimum effective connection length) of the round thread can be comprehensively and accurately measured. According to the method, the overall precision and reliability of thread measurement are remarkably improved, meanwhile, a solid technical guarantee is provided for the safety and durability of oil well pipeline connection, and the method has important reference value for research and application in related fields.

The application discloses a kind of based on industrial CT image blade wall thickness size subpixel level measurement method and system, method includes obtaining the scanning CT image of turbine blade part to be measured;The edge of turbine blade in scanning CT image is extracted using pixel level edge detection algorithm, and the coarse positioning edge position of blade is obtained;The edge point of coarse positioning edge position is interpolated using subpixel edge detection algorithm based on cubic spline interpolation, and the subpixel edge profile of blade is obtained;Least square method is used to curve fitting to subpixel edge profile, and the blade wall thickness size of blade part to be measured is measured based on fitting result.The application combines pixel level edge detection algorithm with subpixel edge detection algorithm based on cubic spline interpolation to process CT image, and the edge pixel point is promoted from pixel level to subpixel level, which not only guarantees the stability of measurement result, but also effectively realizes high-precision measurement of turbine blade size.

The invention relates to the technical field of industrial visual positioning, and provides a positioning precision improvement method and system based on machine vision, and the method comprises the steps: carrying out the fusion processing of multi-view image information based on an image fusion algorithm, and obtaining a fused image; performing sub-pixel edge detection processing on the fused image to obtain a target image; determining the pose of the to-be-positioned target based on the target image; obtaining a standard pose of the to-be-positioned target, and generating a positioning scheme of the to-be-positioned target based on the standard pose and the pose; and positioning the to-be-positioned target based on the positioning scheme, acquiring motion parameter information of production line equipment where the to-be-positioned target is located in real time in the process of positioning the to-be-positioned target, and adjusting the position of the to-be-positioned target in real time based on the motion parameter information. According to the method, the workpiece can be accurately positioned without depending on a fixed calibration device or a laser range finder.

This invention relates to the field of pharmaceutical automation equipment and discloses an intelligent sampling robot suitable for drug production. The robot includes a multi-sensor fusion positioning system, a force control coordination system, and a cleanroom structure design. The multi-sensor fusion positioning system comprises a 3D laser positioning module, a binocular vision module, a sub-pixel edge detection module, and a sensor fusion algorithm. The force control coordination system includes a hybrid RNN-LSTM (Recurrent Short-Term Memory) and CNN (Convolutional Neural Network) network, an airbag gripper, a six-dimensional force sensor, and an underactuated rotation mechanism. The cleanroom structure design includes a mechanical gripper, a nano-sterile coating, and a stainless steel IP69K sealing structure. By employing a fusion algorithm combining extended Kalman filtering and fuzzy evidence theory, efficient fusion of multi-sensor data is achieved. Dynamic threshold adjustment is realized through the hybrid RNN-LSTM and CNN network, reducing force control fluctuations and lowering production costs.

The invention discloses a transverse image distance measuring device and method for transformer replacement traction, and belongs to the technical field of power equipment maintenance. The device comprises a transverse visual image deviation measuring system, a mounting frame, a quick clamping device and an image processing host, the visual deviation measuring system is arranged above a main transformer transfer trolley track wheel carrier through the mounting frame and the quick clamping device, and the visual angle of the visual deviation measuring system is adjusted to be perpendicular to a track; the system collects images of track wheels and tracks; the method comprises the following steps of: based on an image, extracting a track and a wheel rim edge through a sub-pixel edge detection algorithm which comprises image preprocessing and is based on a Zernike moment, and calculating a transverse deviation amount; according to the method, traditional manual plumb line measurement is replaced with non-contact image measurement, the measurement precision is greatly improved, uninterrupted continuous monitoring is achieved, the rail gnawing risk is effectively eradicated, and the operation safety and universality are remarkably improved.

The invention discloses a method and a device for measuring the motion idle stroke and the overall flexibility of a flapping-wing aircraft mechanism, and relates to the field of miniature aircrafts, and the method comprises the steps: loading an aircraft wing rod on a high-speed camera measurement platform, and recording an image of the wing rod in a motion period; after the recorded image is optimized and denoised, an optical flow method is used for capturing a moving wing rod, a sub-pixel edge detection method is used for recognizing a gray level image of wing rod movement, then the edge is optimized and corrected based on the geometrical characteristics of the wing rod, and edge calibration is carried out on the gray level image of wing rod movement; then, the angular displacement of the wing rod is recognized according to the wing rod motion gray level image after coordinate calibration is completed, meanwhile, the distribution load of the wing rod is reconstructed, and inversion is conducted on the bending moment of the wing rod through recognized coordinate information and a Tikhonov regularization method; and finally, drawing an angular displacement-bending moment curve graph of the wing rod and extracting the idle stroke and the overall flexibility of the aircraft. According to the method, the motion idle stroke and the overall flexibility of the mechanism on the flapping-wing air vehicle are effectively extracted with high precision, and the method has great guiding significance in design, test and function research of the flapping-wing air vehicle.

The application discloses a kind of based on edge feature direction code matching's hydraulic hoist tower structure deformation measurement method, belong to computer vision measurement field, this method includes the following steps: using subpixel edge detection method to the detection area image is handled, obtain subpixel edge line position and subpixel edge direction;Based on subpixel edge direction, using direction code encoding method to the edge direction angle is handled, obtain the edge direction code of discretization, and based on the edge direction code of discretization the edge direction code of left and right camera image is calculated;Based on binocular stereo vision system, using template matching method and parallax principle to the edge direction code of left and right camera image is matched, calculate the spatial three-dimensional coordinates of detection area matching coefficient maximum point;Based on the spatial three-dimensional coordinates change of same detection point before and after structure deformation, obtain the deformation of hydraulic hoist tower structure.

This invention relates to the field of printing machinery technology, and particularly to a high-precision positioning method for screen printing based on machine vision, comprising: Step 1: Constructing a collaborative calibration and marking system; Step 2: Dynamic acquisition of deformation data: Within a set time window after the squeegee leaves the screen, images of the first type of marker points and the second type of target are acquired simultaneously; the center of the marker is located using a sub-pixel edge detection algorithm, and the difference between the actual spacing and the nominal spacing is calculated; Step 3: Generation of thermo-mechanical coupling compensation parameters; Step 4: Multi-degree-of-freedom collaborative positioning: Motion control commands for the substrate support platform and the screen tensioning mechanism are generated based on the translation compensation amount and rotation compensation angle; Positioning adjustment is completed and the marker overlap is verified before the start of the next printing cycle; Step 5: Iterative optimization of closed-loop error. This method achieves efficient perception, accurate modeling, and real-time compensation of multi-source errors such as thermal deformation, force-induced deformation, environmental disturbances, and material properties during the screen printing process.

The invention discloses a hydraulic lifting tower structure deformation measurement method based on edge feature direction code matching, and belongs to the field of computer vision measurement, and the method comprises the following steps: employing a sub-pixel edge detection method to process a detection region image, and obtaining a sub-pixel edge line position and a sub-pixel edge direction; processing the edge direction angle by adopting a direction code coding method based on the sub-pixel edge direction to obtain a discretized edge direction code, and calculating the edge direction codes of the left and right camera images based on the discretized edge direction code; based on a binocular stereoscopic vision system, matching edge direction codes of left and right camera images by adopting a template matching method and a parallax principle, and calculating a space three-dimensional coordinate of a maximum value point of a matching coefficient in a detection area; and obtaining the deformation amount of the hydraulic lifting tower structure based on the spatial three-dimensional coordinate change of the same detection point before and after the structure deformation.

The present application relates to the vertical shaft cage detection technical field, specifically, it relates to a kind of industrial intelligent vision detection and diagnosis system for vertical shaft cage, acquisition unit synchronously obtains cage flange surface image and cage car pitch angle, coal dust shadow quantization unit uses bimodal adaptive threshold segmentation combined with region growing algorithm to extract coal dust area, introduce space weight factor to calculate average gray value and coverage area proportion, dynamic coupling calibration unit relies on three-dimensional look-up table and nonlinear interpolation of multi-working condition calibration, match the optimal deviation compensation coefficient of the combination of jitter and coal dust, generate dynamic gray scale segmentation threshold, defect diagnosis decision unit is through zoned adaptive segmentation, multiscale morphological filtering denoising, combined with gradient amplitude and curvature double-constraint subpixel edge detection accurately calculate wear depth, according to real-time depth and historical trend output grading control instruction, eliminate coupling interference, improve cage wear detection accuracy, provide reliable basis for safe operation.

The invention discloses a steel bar sleeve joint spacing detection method based on depth estimation and a MobileSAM model, and the method comprises the steps: obtaining the position information of a steel bar sleeve joint in a steel bar image through a YOLOv8 model of the steel bar sleeve joint, generating a depth map through a Monopth2 model, obtaining the steel bar sleeve joints with the same average depth, and carrying out the detection of the spacing of the steel bar sleeve joint. And obtaining a segmentation mask image of the same layer of steel bar sleeve joints by using a MobileSAM model, and finally detecting the actual spacing of the steel bar sleeve joints by using a sub-pixel edge detection algorithm. According to the method, the YOLOv8 model, the Monopth2 model, the MobileSAM model and the sub-pixel edge detection algorithm are combined, the distance between the steel bar sleeve joints can be detected, and particularly the distance between the steel bar sleeve joints of a multi-layer steel bar mesh can be detected; the problems that in the prior art, the steel bar spacing of a multi-layer steel bar mesh is difficult to detect, and the spacing of the steel bars on the wrong layer is easily calculated to obtain a wrong result are solved, and the accuracy of steel bar spacing detection is improved.

The invention relates to a visual detection method and system, in particular to a high-precision visual detection method and system based on a target detection model. Comprising the following steps: constructing and training a target detection model; positioning a workpiece in the workpiece image by the target detection model, and drawing a workpiece bounding box; carrying out canny edge detection and Zernike moment sub-pixel edge detection on the workpiece in the bounding box to obtain a workpiece image with sub-pixel-level edge lines; the image distortion correction model is adopted to correct the workpiece image with the sub-pixel-level edge lines, and real distance parameters of the workpiece are obtained through the conversion relation model; comparing the real distance parameter of the workpiece with the real distance parameter of a standard workpiece, if the real distance parameter is within a threshold range, determining that the workpiece is qualified, and if the real distance parameter is outside the threshold range, determining that the workpiece is unqualified; and after comparison is completed, high-precision visual inspection of the workpiece is completed. Computer vision and workpiece parameter measurement are combined, and the automation level of industrial production is improved.

This invention discloses a method, apparatus, and storage medium for sub-pixel edge detection of battery cell images. The method includes: acquiring a preprocessed battery cell image; edge localization processing based on the Canny operator to obtain pixel-level edge point images; obtaining an optimal threshold using a maximum entropy multi-threshold segmentation algorithm based on the Firefly algorithm; calculating Zernike parameters for the pixel-level edge points using a Zernike moment sub-pixel edge detection algorithm; separating contour edge points, fine grid edge points, and main grid edge points based on the Zernike parameters and the optimal threshold; and performing least-squares fitting processing on these points to obtain the contour line, fine grid edge line, and main grid edge line of the battery cell. This invention, through the Zernike moment sub-pixel edge detection algorithm, reduces the edge detection accuracy to within one pixel and minimizes the impact of noise on detection accuracy.

The invention relates to a high-precision measurement method for chip mounting geometric parameters, and relates to the technical field of chip mounting geometric parameter measurement. Comprising the following steps: carrying out distortion correction on a patch element image; carrying out image preprocessing on the patch element image subjected to distortion correction; according to the sub-pixel edge detection algorithm based on the Canny-Franklin moment, firstly, Canny edge coarse positioning is improved, and then sub-pixel edge extraction is carried out based on the Franklin moment; edge fitting and size measurement: after sub-pixel-level accurate positioning is carried out on the edge of the element by using an improved Canny-Franklin moment method, straight line fitting is carried out on the edge through a least square fitting straight line algorithm; and carrying out element edge pixel size, deviation angle and centroid calculation. According to the invention, the positioning precision and the detection efficiency in the chip mounting process can be improved.

The present invention discloses a method for restoring sinusoidal motion blurred images based on a priori models. The point spread function of the sinusoidal motion sequence images is calculated by using the gradient optical flow method and the Gaussian fitting method. Based on the prior information of the sinusoidal excitation signal, the motion image at the maximum displacement position is used as a reference frame with a zero point spread function blur kernel. The Lucy-Richardson restoration method and the solved point spread function are used to enhance the characteristic edges of the corresponding sinusoidal motion blurred images to obtain restored images with clear characteristic edges. The noise of the restored images is eliminated by the NL-Means method to suppress the boundary ringing effect of the restored images and improve the signal-to-noise ratio. Finally, objective image quality evaluation indicators and sub-pixel edge detection methods based on Zernike moments are used to evaluate the effect of the restored images and extract the characteristic edges respectively. This method can meet the growing development needs of high-precision linear and angular vibration measurement, pose estimation, target detection, etc. in machine vision.

The invention discloses a sub-pixel edge detection method based on an improved Franklin moment. A difference method is used for carrying out difference calculation on a target pixel and adjacent pixels in the x direction and the y direction and carrying out judgment, convolution templates in different directions are adopted for calculating a result through a judgment result, calculation can be carried out in the direction with stronger information in a targeted mode, the situation that the whole picture participates in convolution operation is reduced, and the calculation efficiency is improved. And meaningless calculation in the direction without special meanings is reduced. And through the characteristic that the pixel gray scale distribution symmetry of the even-size template on the left and right sides of the edge is better, a 6 * 6 template of the Franklin moment is deduced and calculated, interval judgment is carried out on the target pixel, and a corresponding convolution template is adopted to calculate the sub-pixel value of the target. The time required for edge detection is greatly reduced by reducing the template size of the Franklin moment and reducing the number of convolution templates needing to be calculated.

The application relates to a multi-camera large-size visual measurement method and system, which comprises the following steps: installing a plurality of cameras for large-size measurement; marking a group of three-coordinate marker points in the visual field of each camera on a calibration whiteboard by using a three-coordinate measuring machine, and recording the three-coordinate positions and sequence of each three-coordinate marker point; acquiring images of the three-coordinate marker points in the visual field of each camera on the calibration whiteboard by the camera, and obtaining the center positions of the regions where the three-coordinate marker points are located; obtaining the pose transformation matrix of all the cameras; adopting a sub-pixel edge detection algorithm based on Gaussian fitting on the images collected by the camera to obtain an edge point set, and fitting an edge straight line by using the edge point set; obtaining a straight line intersection point based on the edge straight line; converting the straight line intersection point to a world coordinate system based on the obtained pose transformation matrix; and obtaining a size based on the converted position. The application is used for high-precision large-size visual measurement.

The present invention relates to the field of image processing technology, and specifically discloses a sub-pixel edge detection method, including: a coordinate system establishment step, establishing a coordinate system in the collected image information; image preprocessing, performing image preprocessing on the image information to obtain a target image; an edge detection step, performing edge detection on the laser spot and the marking circle in the target image respectively, obtaining the spot edge information and the marking circle edge information; a position calculation step, calculating the spot position information of the laser spot and the marking circle position information of the marking circle in the coordinate system according to the spot edge information and the marking circle edge information; an offset calculation step, calculating the position offset of the laser spot moving to the marking circle according to the spot position information and the marking circle position information; and a correction step, controlling and adjusting the laser spot position projected by the laser cross-section detector according to the position offset. The alignment accuracy of the laser spot and the marking circle is higher when the technical solution of the present invention is adopted.

The application discloses a target positioning method based on Zernike moments and geometric constraints, which comprises the following steps: collecting images, and performing interpolation after cutting out the area where the target is located; adopting an improved Zernike moment sub-pixel edge detection algorithm, and utilizing the geometric relationship of the target pattern to adaptively adjust the background gray threshold value in the Zernike moment, so as to realize edge extraction of the target object; and utilizing the edge coordinates of the target pattern to perform target positioning. The application can solve the problems of edge positioning deviation and pseudo edge of the traditional Zernike moment sub-pixel edge detection method, can adaptively adjust the edge parameter threshold value of the Zernike moment sub-pixel detection, has better robustness, and improves the accuracy of edge extraction.

The invention discloses a bridge expansion joint disease monitoring method and system based on time sequence image analysis, and belongs to the technical field of bridge structure health monitoring, and the method comprises the steps: installing fixed industrial cameras at the two sides of an expansion joint, regularly collecting high-resolution images, and synchronously obtaining temperature data; accurate registration of images in different periods is realized through feature point matching; identifying diseases such as rubber strip aging, profile steel warping, foreign matter blockage and concrete damage by adopting a deep convolutional neural network; a sub-pixel edge detection algorithm is utilized to measure the seam width, and a seam width-temperature response curve is established to identify abnormal deformation; according to the method, automatic detection, high-precision measurement and trend early warning of expansion joint diseases are realized, and the method is suitable for remote intelligent supervision of expressway bridge groups.

The application discloses an oil saw guide plate flatness and groove wear image measurement method and system, relates to the technical field of machine vision image measurement, and the method obtains guide plate multi-source fusion image data through the cooperative collection of a line laser projection and coaxial light source illumination, calculates the flatness deviation by using three-dimensional point cloud reconstruction and reference plane fitting, adopts sub-pixel edge detection and multi-section statistical measurement to quantize groove wear parameters, fuses the flatness and groove wear indexes to calculate a comprehensive wear index and output a grading maintenance suggestion, and automatically generates a detection report, thereby providing an objective quantitative basis for oil saw guide plate quality detection.

The present invention relates to a method for positioning circular holes and detecting deformation of stamping parts based on sub-pixel edge extraction, including: 1. Collecting images of stamping parts and performing preprocessing; 2. Using the OSTU threshold segmentation algorithm to extract the overall target of the stamping parts, and then converting the image into a binary image; 3. Extracting all edge pixels of the stamping parts through sub-pixel edge detection, and then performing sub-pixel straight edge fitting and sub-pixel curve edge fitting to obtain all contour edges of the three types of straight lines, ellipses, and circles, screening out the straight lines of the two-side edge contours and all circular contour edges, and calculating the center coordinates; 4. Calculating the distances from each center to the two-side edge straight lines respectively to determine whether the positions of the circular holes are shifted; 5. Calculating the distances from each center to each point on the circular edge contour, selecting the maximum value and the minimum value for comparison, and if the difference between the two exceeds the set range, it is determined that deformation has occurred. This method is beneficial to improving the accuracy and efficiency of positioning circular holes and detecting deformation of stamping parts.

The invention provides a parachute image scale factor solving method. The scale factor is the ratio of the actual size of the parachute structure to the size of the parachute structure in the image. The method comprises the following steps: firstly, selecting a canopy top hole as a reference object, extracting the outer contour of the canopy top hole by using a sub-pixel edge detection technology, counting the number of pixel points occupied by the outer contour, and solving to obtain a scale factor corresponding to a top hole plane; then, establishing an image coordinate system and a camera coordinate system in a parachute system, and solving according to a camera imaging model to obtain a relationship between scale factors corresponding to different canopy sections; finally, a canopy deformation theoretical model in the parachute unfolding process is established, the optical distances from different sections of the canopy to the camera are obtained, and therefore scale factors corresponding to key planes such as the maximum projection section of the canopy are obtained through calculation. According to the method, the parachute image scale factor solving efficiency and precision are improved, and a foundation is laid for high-precision parameter identification of the parachute image shot by the monocular camera.

The application discloses a fish body comprehensive detection method and system, computer equipment and a storage medium. The method comprises the following steps: acquiring a main view image and a top view image of a fish body through different-view-angle depth cameras; adopting a trained target detection model to perform target detection on the main view image and the top view image, and determining initial position information and species information of the fish body; performing sub-pixel edge detection on the initial position information of the fish body to obtain edge position information of the fish body; obtaining coordinate information of the fish body edge in a corresponding camera coordinate system according to the edge position information of the fish body and in combination with depth information of the depth camera, and obtaining fish body size information through calculation of Euclidean distance; and predicting the fish body weight according to the fish body size information and the fish body species information, so as to obtain a fish body growth state. The application improves the accuracy of fish body growth state detection, and can timely find abnormal conditions of fish body growth.