608results about How to "Increase contrast" patented technology

The invention discloses an intelligent optimization method for ultrasonic image quality enhancement. The intelligent optimization method comprises the following steps: acquiring an original ultrasonic image through ultrasonic imaging equipment; performing multi-stage preprocessing on the obtained original ultrasonic image; extracting multi-scale features from the ultrasonic image subjected to the multi-stage preprocessing; performing intelligent scoring on the images subjected to multi-stage preprocessing and multi-scale feature extraction; dynamically optimizing the processing parameters under the guidance of the image quality score; identifying and emphatically enhancing a target area in the ultrasonic image; fusing a local optimization result with the global image; and outputting the optimized ultrasonic image and the corresponding quality scoring report. Compared with the prior art, the ultrasonic image quality enhancement method has the following advantages and effects that the ultrasonic image quality enhancement method provided by the invention has remarkable innovativeness in technology, solves the ultrasonic image quality problem existing in the prior art, provides a more efficient and accurate auxiliary tool for clinical diagnosis, and has wide application prospect and practical value.

The invention provides a satellite-borne multi-band synthetic aperture radar signal processing optimization method and system, and relates to the technical field of signal processing, and the method comprises the steps: obtaining original echo data of a satellite-borne multi-band synthetic aperture radar, carrying out Doppler center frequency estimation, carrying out two-dimensional matrix product operation, and obtaining compensation echo data; distance direction position information and scattering intensity information are obtained based on a distance direction matching filtering algorithm of sub-band decomposition; determining spatial distribution density, performing adaptive block processing, and performing phase error modeling in each block to generate a continuous phase correction function; performing wavelet domain convolution operation to obtain corrected echo data, obtaining a multi-band two-dimensional image, extracting a multi-dimensional feature vector through a deep neural network, performing weight learning, performing nonlinear weighted fusion, and performing adaptive histogram equalization enhancement to determine a synthetic aperture radar imaging result.

The invention discloses a method for identifying impurities and bubbles in a wine bottle based on multi-feature comprehensive judgment, and belongs to the technical field of image processing and computer vision, and the method comprises the steps: employing a high-resolution industrial camera to be combined with an LED light source, and continuously shooting multiple frames of images of the wine bottle under the consistent shooting angle and illumination condition; carrying out the preprocessing of the collected image through employing a self-adaptive histogram equalization and Gaussian blur method; extracting the edges of impurities and bubbles in the wine bottle on the image by adopting a Canny edge detection algorithm to obtain an edge image; segmenting the edge image into a plurality of target areas by using a contour detection technology, and extracting possible bubble and impurity areas from the edge image; extracting target area features including circularity, gray average, gray uniformity, texture features and color space; and according to the extracted features, bubbles and impurities are distinguished by applying a comprehensive judgment rule. According to the method, the bubbles and the impurities are comprehensively judged by adopting multi-feature combination, so that the accuracy and the robustness of detection are greatly improved.

The invention relates to a power station screen cabinet equipment temperature early warning method and device, computer equipment, a computer readable storage medium and a computer program product. The method comprises the following steps: acquiring a dual-light image of power station screen cabinet equipment to be analyzed according to acquisition parameters corresponding to current acquisition; the dual-light image comprises an infrared thermal imaging image and a visible light image; performing preprocessing, feature extraction processing, spatial registration processing and information fusion processing on the dual-light image to generate a fused image; performing temperature field reconstruction processing and hot spot detection processing on the fused image to obtain temperature field distribution information and hot spot area information; and according to the temperature field distribution information and the hot spot area information, performing temperature analysis on the power station screen cabinet equipment to obtain temperature early warning information of the power station screen cabinet equipment. By adopting the method, the temperature early warning accuracy of the power station screen cabinet equipment can be improved.

The embodiment of the invention discloses a bridge crack width monitoring method. The method comprises the following steps: acquiring a continuous video image of a bridge surface; generating a pixel-level segmentation mask image of the crack by using the target detection model; when the target detection model is pre-trained, optical flow features between adjacent frames in the first data set are extracted through an optical flow calculation algorithm, and deep fusion is performed on the optical flow features and the image features; contour pixel points of the crack are extracted based on the crack segmentation mask pattern, and the width change information and the width change rate of the crack are obtained; according to the width change information and the width change rate, performing trend prediction on the crack width change by using a time sequence prediction model to obtain a prediction result of the crack width change; in the training process of the time sequence prediction model, a basic model and a complexity capture module are designed, and the weight of a main model is optimized by using a weighted fusion strategy. According to the invention, efficient and accurate crack width monitoring is realized through crack identification and prediction.

The invention discloses a visual detection system for an automobile door panel assembly, and relates to the technical field of automatic detection, and the visual detection system comprises the steps: installing a high-precision industrial camera, and carrying out the synchronous collection of the multi-view image data of an automobile door panel; preprocessing the acquired multi-view image data of the automobile door panel; carrying out detail enhancement on the preprocessed multi-view image data of the automobile door panel by adopting a super-resolution algorithm; integrating the enhanced multi-view image data of the automobile door panel into an automobile door panel panorama through binocular stereoscopic vision matching, and constructing an automobile door panel three-dimensional model; the automobile door panel three-dimensional model is compared with a standard automobile door panel three-dimensional model, and the buckle defect in the automobile door panel is recognized; and carrying out position marking and defect type analysis on the identified defect points, and generating an automobile door panel detection report according to the defect type analysis. According to the invention, details are enhanced by using a super-resolution algorithm, defects are accurately positioned by using a three-dimensional model, and the detection efficiency and accuracy are improved.

The invention relates to the field of image enhancement, in particular to an image enhancement method and system of a vehicle-mounted image sensor. The method comprises the following steps: acquiring driving record images of a vehicle at multiple angles; dynamic scene self-adaptive exposure adjustment is carried out on the driving record images of the vehicle at the multiple angles, so that multiple self-adaptive exposure driving images are obtained; carrying out deep network noise identification on the plurality of adaptive exposure driving images, and carrying out dynamic filtering and noise reduction on sub-images one by one so as to generate a plurality of filtering and noise reduction optimized images; carrying out image texture analysis on the plurality of exposure sub-images, and carrying out texture distortion reconstruction to construct a plurality of texture reconstruction images; and road object visual identification is carried out on the plurality of texture reconstruction images, and adaptive contrast enhancement processing is carried out, so that a plurality of detail contrast enhancement images are constructed. According to the invention, a high-definition and stable panoramic vehicle-mounted image is realized.

The invention relates to the technical field of image enhancement, in particular to a method for detecting an intestinal lesion area of a child based on machine vision. Carrying out threshold segmentation on the grayscale image to obtain a reference point; clustering the reference points and partitioning the grayscale image according to the shape of a cluster; acquiring edge points and a to-be-optimized area according to the gradient of the pixel points in the block area; obtaining a target point according to the gray difference characteristics of the pixel points and the edge points of the to-be-optimized region; obtaining an abnormal point according to the gray difference characteristics of the target point and other target points in the neighborhood and the gray difference characteristics of the neighborhood of the edge points; and obtaining a gray adjustment range value according to the gray difference characteristics of the neighborhoods of the abnormal points and the edge points in the to-be-optimized region. According to the method, a final gray value is obtained according to a gray adjustment range value and a gray discrete feature of an edge point neighborhood; histogram equalization is carried out according to the final gray value, an intestinal tract detection enhanced image is obtained, and the image enhancement accuracy is improved.

The invention discloses a power transmission and transformation equipment inspection infrared and visible light image fusion method, which comprises the following steps: S1, acquiring an infrared image and a visible light image of power equipment, constructing an image data set of the power equipment, and dividing the image data set into a training set and a test set; s2, constructing an infrared and visible light image fusion network model EIVFusion; s3, training the infrared and visible light image fusion network model EIVFusion by using the infrared image and the visible light image of the power equipment in the training set to obtain a trained infrared and visible light image fusion network model EIVFusion; and S4, inputting the infrared image and the visible light image of the power equipment in the test set into the trained infrared and visible light image fusion network model EIVFusion to obtain a fusion image. According to the method, the features in the visible light image and the infrared image can be well extracted and fused together, and high-performance fusion of the infrared image and the visible light image for power transmission and transformation equipment inspection can be realized.

The invention discloses a remote sensing image haze removal processing method and system, and relates to the technical field of image processing. The method comprises the steps of obtaining a to-be-processed remote sensing image; extracting wave band data of a plurality of wave bands in the remote sensing image to be processed, and determining a reference wave band and a plurality of target wave bands based on the wave band data; based on the reference wave band, determining an atmosphere illumination parameter, and calculating a transmittance distribution diagram of each target wave band; for each target wave band, carrying out haze removal processing on the target wave band according to the atmosphere illumination parameters and the transmittance distribution diagram to obtain an initial haze removal image of the target wave band; and performing fusion processing on the initial haze-removed images of the target wavebands to obtain a haze-removed image corresponding to the to-be-processed remote sensing image. By implementing the technical scheme provided by the invention, the effect of improving the accuracy of haze removal processing of the remote sensing image is achieved.

The invention provides an image enhancement optimization method based on multi-strategy improved sparrow search. The method comprises the following steps: performing normalization processing on a gray value of each pixel point of an original image; initializing a sparrow population and parameters according to an elite chaos reverse learning strategy; the method comprises the following steps: preprocessing an input image by adopting a dual gamma function BiGA; calculating the value of the evaluation function; updating the optimal fitness value of the sparrow population; when an iteration stop condition is not met, updating the position of the sparrow population by adopting a probabilistic following strategy, a self-adaptive local search strategy and a Cauchy Gaussian mutation strategy, and updating parameters of a dual gamma function BiGA; otherwise, ending iteration, and calculating an optimal parameter; processing the image, and outputting an enhanced image; according to the method, the visual effect of the image can be improved, edge details and textures can be richer, and the enhanced image can have higher contrast and better definition. The method is best in overall enhancement effect, and the obtained image is better in quality.

The invention provides a folate receptor targeting small molecule compound, a preparation method, a composition and application. Specifically provided is a compound represented by formula I or a pharmaceutically acceptable salt thereof. The compound or the pharmaceutically acceptable salt thereof provided by the invention can be used as a small-molecule targeting fluorescence contrast agent for assisting surgical treatment, has the advantages of novel structure, good solubility, small steric hindrance and strong targeting property, can obviously improve the view field of tumor surgery, and is beneficial to efficiently and accurately implementing excision of pathological tissues such as tumor tissues, boundary tissues and the like. # imgabs0 #

The invention discloses an image enhancement method based on high-frequency and low-frequency adaptive fusion. The method comprises the following steps: respectively carrying out contrast enhancement of color channels on original images to obtain intermediate feature maps; calculating pixel mean values of the intermediate feature maps under the color channels; performing color compensation on the intermediate feature map according to the pixel mean value to obtain a color reconstruction image; decomposing the color reconstruction image into high-frequency image features and low-frequency image features; performing normalization processing and gamma correction on the high-frequency image features to obtain a high-frequency corrected image; sharpening the high-frequency correction image to obtain a high-frequency recovery image; carrying out defogging processing on the low-frequency image feature weight to obtain a low-frequency recovery image, and carrying out adaptive fusion on the high-frequency recovery image and the low-frequency recovery image to obtain an output image; rich detail and edge information is provided by the high-frequency recovery image, and optimized background and color information is provided by the low-frequency recovery image, so that the visualization effect of the output image is remarkably improved.

The invention relates to the technical field of display, and discloses a Mini LED dynamic backlight module control method and system.The method comprises the steps that the component state of a Mini LED dynamic backlight control system is analyzed, and an ambient light-backlight brightness model and a user state-backlight brightness model of a watching user are constructed; coupling the ambient light-backlight brightness model and the user state-backlight brightness model to obtain a coupled backlight brightness model, and defining the initial backlight brightness of the Mini LED corresponding to the Mini LED dynamic backlight control system; receiving a display content signal of the Mini LED, extracting display elements of the display content signal and display element characteristics of the display elements, and analyzing display content of the display content signal and a display content type of the display content signal; and analyzing the dimming parameters of the Mini LED by using a content self-adaptive dimming algorithm, sending the dimming parameters to a Mini LED driving circuit in the Mini LED dynamic backlight control system, and executing the dynamic backlight control of the Mini LED through the Mini LED driving circuit. According to the invention, the comfort level of the user in watching experience can be improved.

The invention relates to the technical field of attendance management, and discloses an electronic class board attendance system for palm vein recognition, which comprises a palm vein data acquisition module for irradiating the palm of a user through a near-infrared light source and acquiring a palm vein image; the image optimization processing module is used for performing optical modeling optimization, noise removal and quality enhancement on the acquired palm vein image; the deep learning recognition module is used for extracting palm vein features and performing identity matching; the attendance record storage module is used for storing and managing user attendance data; and the control and communication module is used for managing cooperative work of all the modules and performing data interaction with an external attendance database. Through the technical scheme of combining near-infrared illumination and the light transmission equation, the palm vein feature image is accurately extracted, the effect of optimizing palm vein contrast and detail definition is achieved, the problems of palm vein feature edge blurring and detail loss are solved, and the image processing quality is improved.

The invention discloses a cancer image contrast enhancement method and system based on deep learning, belongs to the technical field of medical image processing, and adopts an adaptive histogram equalization algorithm for preprocessing to enhance the contrast of a local region. Texture, shape and edge features are extracted through multi-scale analysis, and feature descriptors are constructed. And based on the feature descriptor, segmenting the image by using a region growing segmentation algorithm to obtain a focus region boundary. A self-adaptive contrast enhancement technology is adopted for the segmented focus area, and the focus display effect is highlighted. In consideration of noise interference, a denoising method based on wavelet transform is introduced, noise is suppressed in a high-frequency sub-band, and details are reserved in a low-frequency sub-band. Through the organic combination of the above technical schemes, the contrast and definition of the cancer image can be effectively enhanced, the focus area is highlighted, and noise interference is inhibited at the same time.

The invention discloses a virtual dynamic three-dimensional image fusion method and system based on pre-registration, and the method comprises the steps: obtaining a static three-dimensional image of a target region, and a dynamic three-dimensional CBCT image which at least comprises one respiratory cycle; performing breathing phase division on the dynamic three-dimensional CBCT image; obtaining a corresponding three-dimensional image frame on each phase node; registering the plurality of three-dimensional image frames obtained by division with the static three-dimensional image in sequence to obtain a single-frame deformation field; collecting breathing signals of a patient in real time during an operation; performing interpolation calculation on the single-frame deformation field based on the respiratory signal, and generating a complete deformation field in the whole respiratory cycle in real time; and applying the complete deformation field obtained by interpolation to the static three-dimensional image, so that the static three-dimensional image is virtually dynamic. Through a method of pre-registration of a pre-operative breathing phase node image and interpolation based on a real-time breathing phase in an operation, the calculation amount during real-time fusion in the operation is reduced, so that ultra-low-delay image fusion is realized.

The invention discloses a medical ultrasonic image quality control system based on multi-modal fusion. The medical ultrasonic image quality control system comprises a data preprocessing module used for collecting medical ultrasonic image data of different modals from different crowds in real time and preprocessing the medical ultrasonic image data; the dynamic self-adaptive fusion module is used for dynamically adjusting a fusion strategy according to the medical ultrasonic image data acquired in real time and introducing a self-adaptive mechanism to dynamically adjust weights of different modal data; the image enhancement module combines medical ultrasonic image data with a three-dimensional anatomical structure of a human body by utilizing an augmented reality technology to generate a visual and three-dimensional diagnostic image; the visualization module adopts a dynamic image display mode to present the change process of the diagnosis image in real time; a complete medical ultrasonic image quality control system is constructed by integrating a data preprocessing module, a dynamic adaptive fusion module, an image enhancement module, a visualization module and the like, the system can automatically process and analyze medical ultrasonic image data, and the quality control efficiency and accuracy are improved.

The invention discloses a diseased fish detection method and device, and belongs to the technical field of computer vision detection. In order to solve the problems that in the prior art, diseased fish disease detection efficiency is low, accuracy is not high, and real-time detection cannot be achieved, a diseased fish detection model is constructed, and the diseased fish detection model comprises a backbone network, a neck network and a detection head network; the method comprises the following steps: inputting a diseased fish image into a backbone network based on a spatial deep convolutional neural network for feature extraction to obtain a visual feature map, and then inputting the visual feature map into a contrast-enhanced attention transfer structure of a neck network for feature fusion processing to obtain a fused feature map; and the fusion feature map is input into a detection head network, and a sick fish target detection result is obtained through detection of a multi-head prediction small target detection algorithm, so that fish school disease detection is performed in real time, the detection efficiency and detection precision of fish diseases are effectively improved, and the method has relatively high practicability and application universality.

The invention relates to the technical field of single cell data processing, in particular to a database visualization method based on single cells. The method comprises the following steps: acquiring a single cell data set; standardizing the single cell data set to obtain standardized single cell data; performing high-dimensional feature extraction on the standardized single-cell data to obtain single-cell high-dimensional feature data; and performing nonlinear dimension reduction processing on the single-cell high-dimensional feature data to obtain single-cell dimension reduction data. Performing data clustering on the single cell dimension reduction data to obtain single cell clustering data; performing gene differential expression extraction on the single cell clustering data to obtain differential expression gene data; multi-modal data fusion analysis is carried out on single cell data through a data processing technology, a feature mapping technology and a visualization technology; and dynamic weight division is carried out on the single cell database, so that the effect of visualization of the single cell database is enhanced.

The invention discloses a dual-light camera image registration fusion method, system and device based on binocular distance measurement and a storage medium, which are applied to the technical field of image processing, and the method comprises the steps: carrying out the distortion correction of a dual-light image, obtaining the internal and external parameters of a dual-light camera, and carrying out the stereo correction and mapping transformation of the dual-light image based on the internal and external parameters; performing parallax estimation calculation on the corrected image by using an SGBM stereo matching method, calculating a distance from a target to the dual-light camera according to a value in a center range in the image, and extracting corresponding preliminary registration parameters and an affine transformation homography matrix according to a registration parameter index table to perform image registration; and performing pixel value proportion conversion on the dual-light image after image registration, extracting high-frequency information and low-frequency information of the infrared image, and performing fusion of the infrared image and the visible light image based on the high-frequency information and the low-frequency information. According to the method, the real-time performance and practicability of image registration fusion are effectively improved, and the image registration fusion precision is further improved.

The invention relates to the technical field of head-up display, in particular to an HUD optical aberration correction method, system and device and a vehicle. The correction method comprises the following steps: generating a light beam through an LED light source or a laser light source, and adjusting the brightness and color parameters of the LED light source or the laser light source through a control circuit so as to adapt to different display requirements and environmental conditions; and the light beam is preliminarily shaped and collimated by the light beam shaping element until a proper divergence angle and a proper light spot size are met so as to meet the requirements of a coupling element. According to the invention, the light beam emitted by the light machine is subjected to one-dimensional or two-dimensional pupil expansion through the optical waveguide, and then view field expansion and aberration correction are carried out through the free-form surface mirror, so that the problems of large size, limited view field angle, small eye box range and insufficient imaging definition in the prior art are effectively solved.

The invention discloses a low-illumination image enhancement algorithm based on deep convolution, which is suitable for the field of image processing, and the core of the algorithm lies in an efficient noise reduction process and accurate decomposition of an illumination image and a reflection image. Comprising the following steps that firstly, a channel noise reduction module works cooperatively through multiple noise reduction strategies, noise in an image is effectively removed, and a foundation is laid for follow-up processing; then, the illumination image decomposition network and the reflection image decomposition network accurately separate the brightness information, texture and color information of the image based on a deep learning structure. And finally, by ingeniously fusing the illumination image and the reflection image, reconstructing a high-quality enhanced image, remarkably improving the brightness, contrast and details of the image, and meanwhile, retaining the natural color of the image. The algorithm has the advantages of being high in noise reduction capacity, accurate in illumination image decomposition, efficient in reflection image decomposition, good in enhancement effect and the like, and an effective and practical solution is provided for image enhancement under the low-illumination condition.

The invention relates to the technical field of capsule appearance defect detection, in particular to an intelligent detection system for capsule appearance defect recognition. The system comprises an image partitioning module used for obtaining a sub-image corresponding to a grayscale image of a capsule image; the gray level distribution similarity obtaining module is used for calculating the gray level distribution similarity of the sub-images; the target degree acquisition module is used for acquiring grey-scale map change feature similarity of the sub-images so as to obtain target degrees of the sub-images; the importance degree calculation module is used for acquiring a wave crest region and a wave trough region corresponding to the subgraph; obtaining the importance degree of the wave crest region of the important sub-graph; the trend distribution part acquisition module is used for a trend distribution part of each wave crest region of the non-important sub-graph; calculating a trend distribution part of each peak region of the important sub-graph; the defect detection module is used for enhancing the capsule image; and identifying the appearance defect of the capsule by using the enhanced capsule image. The accuracy of capsule appearance defect detection can be improved.

The invention belongs to the technical field of computer vision and image processing, and particularly discloses a video defogging method and system based on enhanced alignment, and the method comprises the steps: carrying out the color mapping of a plurality of foggy images through a color lookup table, and obtaining a plurality of enhanced images; based on the enhanced image corresponding to the foggy target frame and the enhanced image corresponding to each foggy reference frame, performing space-time optical flow estimation to obtain a three-dimensional optical flow; performing image transformation on the foggy reference frame based on the three-dimensional optical flow to obtain an alignment frame; and based on the alignment frame, the foggy target frame and the enhanced image corresponding to the foggy target frame, performing splicing and feature fusion to obtain a defogged image corresponding to the foggy target frame. According to the method, details of the foggy image are enhanced, a more accurate alignment result is obtained, more inter-frame information is provided through accurate alignment frames, the network can be effectively helped to learn a better defogging capability, the utilization capability of the deep neural network on the inter-frame information can be improved, and a better video defogging effect is achieved.

The invention discloses a photoelectric wafer defect detection method, and belongs to the technical field of semiconductors. The detection method comprises the steps that the surface of a photoelectric wafer is covered with a quantum dot precursor solution, annealing is carried out, after a quantum dot film is formed, irradiation is carried out under ultraviolet light, a displayed fluorescence signal can mark defects on the surface of the photoelectric wafer, and the purpose of detecting the defects of the photoelectric wafer is achieved. The quantum dots used in the invention have excellent fluorescence efficiency and stability, and can provide a high-contrast marking effect in an SEM image. Therefore, the detection of micron-scale and nano-scale defects is more accurate, and the details are clearer. A traditional fluorescence labeling solution is limited in infiltration capacity in nanoscale defects and can only cover the surface. The quantum dots can penetrate into the defect to form a mark with the depth ratio as high as 1: 3. The depth marking capability is helpful for comprehensively knowing the internal structure of the defect and the defect extension condition. In addition, the quantum dots have excellent adaptability and multifunctionality, and show good stability and coupling effect under different types of wafer conditions.

The present invention provides the use of organic materials in the preparation of color-changeable polymer materials, and also provides color-changeable polymer materials prepared based on such organic materials and a preparation method thereof, belonging to the technical field of polymer laser processing. The present invention adds a specific organic material as an additive to a polymer to obtain a color-changeable polymer material, and the polymer material has an excellent color-change effect. Under the action of a laser, various color marks can be formed on the polymer material, and the obtained color marks are clear and flat with high contrast. At the same time, the polymer material is light in color, and color marks on light-colored, white or transparent polymer substrates can be realized, expanding the application range of laser color marks. In addition, the amount of organic material used in the color-changeable polymer material of the present invention is small, and the preparation method is simple, easy to operate, safe, and low in energy consumption, which is very suitable for large-scale industrial production.

The invention provides a composition containing folic acid compounds and a near-infrared fluorescence capture system. The near-infrared fluorescence capture system comprises an imaging module, and the imaging module comprises a compound shown in the formula I or pharmaceutically acceptable salt of the compound. The compound shown in the formula I or the pharmaceutically acceptable salt of the compound can be used as a near-infrared fluorescence imaging agent, and is good in targeting property and clinical application prospect. # imgabs0 #

The invention relates to the technical field of underwater image enhancement processing, in particular to a pyramid network-based underwater image dynamic enhancement method and application, and the method comprises the steps: decomposing an input underwater image into layered image groups; using a deterministic color mapping network to convert the low-frequency layer input image into a low-frequency layer output image; using a double convolution attention module to obtain enhanced input features; using a packet expansion feedforward network to obtain a pixel-by-pixel mask; performing operation on the enhanced input features and step-by-step pixel masks to obtain high-frequency components; and reconstructing the low-frequency layer output image and the high-frequency component by using a Laplacian pyramid network to obtain an enhanced output underwater image. In conclusion, the underwater image can be dynamically enhanced, the adaptability is high, the method is suitable for various water bodies, color distortion can be effectively corrected, noise introduction is restrained, more image details can be reserved, and the underwater image has better visibility and higher recognition degree.

The invention relates to the technical field of three-dimensional measurement, in particular to a linear structured light bearing bush and gear three-dimensional measurement method and system, and the method comprises the following steps: 1, data collection: employing a CCD camera arranged on a measurement platform to shoot laser images containing laser line patterns from different angles; step 2, image processing: extracting feature points in the laser image by using a computer vision algorithm; and 3, three-dimensional reconstruction: based on the known CCD camera parameters and the position change of the feature points on the to-be-measured object, obtaining three-dimensional data of the to-be-measured object by using a space coordinate conversion technology. And step 4, error correction: carrying out error analysis on the obtained three-dimensional data. And step 5, result output: outputting final three-dimensional measurement data of the to-be-measured object based on the corrected result. Through the automatic and intelligent measurement means, the measurement period can be further shortened, and the production efficiency is improved.