577 results about "Deblurring" patented technology

The invention provides a multi-stage turbulent dynamic video recovery method based on a physical model, and relates to the technical field of image processing. According to the method, the dynamic benefit index DEI is introduced, the turbulence intensity, the dynamic region ratio and the optical flow diagram are combined, the video dynamic intensity under different turbulence conditions is accurately quantified, the high-dynamic turbulence data set is constructed, and the model training and evaluation pertinence is enhanced. In addition, according to the multi-stage recovery frame PMR provided by the invention, a recovery task is decoupled into three stages of de-inclination, motion segmentation enhancement and deblurring, and step-by-step recovery is carried out, so that complex distortion is more accurately dealt with, and efficient generalization and high-quality recovery are realized. The frame is also combined with a dual-stage registration strategy, and through global motion alignment and pixel-level optical flow correction, camera shake and optical interference in a real scene are reduced, the problem of multi-frame dislocation in a turbulent dynamic scene is effectively solved, and the stability and robustness of recovery are improved.

The invention relates to the technical field of fabric detection, and discloses a fabric defect intelligent detection method and system based on AI visual identification. According to the method, motion blur is quantized through motion state data, optical blur caused by fabric motion is eliminated through deconvolution solution, so that motion interference in the fabric transmission process is processed in a targeted mode, self-adaptive balance of the deblurring capacity and the feature retention capacity is achieved, and then based on the optical interference principle, the deblurring capacity and the feature retention capacity are improved. Through a dynamic calibration system combining hardware-level real-time compensation and multi-dimensional optical parameter calibration, dynamic optical parameter calibration of primary correction data is realized, then fabric defect characterization data is extracted to accurately obtain defect features, and finally, a detection-production line control closed loop is constructed through a quality quantitative index and a comprehensive risk value, so that fabric defect detection is realized. The fabric defect detection precision can be improved, so that the problem of high defect missing detection and false detection rate caused by optical data distortion due to movement and environment interference in a traditional method is effectively solved.

The invention discloses an image deblurring system and method based on Transform and multi-scale feature fusion, and relates to the technical field of image processing, the system comprises an input layer, a processing layer and an output layer, the processing layer comprises a multi-input multi-output encoder-decoder module, the multi-input multi-output encoder-decoder module is used for extracting features from blurred images of different scales through an encoder, and outputting the features to the output layer; a clear image is gradually restored based on the extracted features through a decoder; the multi-feature multi-scale fusion module is used for integrating local and global feature information and enhancing the learning ability of the system to global features; according to the improved Transform module, a self-adaptive window mechanism and a cross-channel attention mechanism are introduced, so that the system can better adapt to different types of blurred images, and the effects of feature extraction and image restoration are improved; and the attention fusion module dynamically learns and integrates useful feature information by introducing a channel attention mechanism. The image deblurring method is suitable for various image deblurring application scenes, and image detail features can be effectively recovered.

The invention discloses a fishing boat name identification method and system based on image processing and deep learning. The method comprises the following steps: S1, fishing boat image acquisition; s2, image preprocessing and enhancement are carried out to realize image noise reduction, deblurring and brightness adjustment processing, and image quality judgment is carried out after processing to form closed-loop optimization; s3, self-adaptive image enhancement is carried out to realize illumination compensation and image dynamic enhancement; s4, ship name area detection and segmentation; s5, ship name region feature extraction and optimization; s6, feedback and optimization of an identification result; s7, character recognition and post-processing; s8, performing correction and verification; s9, log identification and report generation; and S10, displaying and storing a result. According to the invention, the fishing boat name identification precision can be effectively improved.

The invention discloses an event information guided image deblurring and high-frame-rate reconstruction method. The method comprises the following steps: step 1, obtaining a blurred image and an event stream corresponding to the blurred image to construct a training data set; 2, constructing a deblurring and frame insertion combined reconstruction network based on a dynamic cross-modal fusion module; step 3, training to obtain a trained joint reconstruction network based on the cross-modal attention mechanism and the UNet architecture; and step 4, inputting a to-be-reconstructed blurred image and a corresponding event stream into the trained deblurring and frame insertion combined reconstruction network based on the dynamic cross-modal fusion module to obtain a final multi-frame reconstruction image. According to the method, a higher weight dynamic state is given to an event mode in a high-speed motion scene, an image data weight is added in a static scene, and a scale feature fusion strategy is adopted for a seriously fuzzy scene, so that the problems of semantic difference and noise interference are reduced, and the image recovery quality of a fuzzy region is improved.

According to the image deblurring method based on adaptive amplitude filtering and gradient enhancement, a U-shaped network architecture is adopted, frequency domain information is optimized through an adaptive time-frequency fusion strategy, and artifacts are reduced. The method comprises a training stage and a testing stage, and comprises the following five steps: S1, in the training stage, obtaining an image deblurring data set and preprocessing an image; s2, constructing an image deblurring model based on adaptive amplitude filtering and gradient enhancement; s3, optimizing a loss function; s4, training the constructed image deblurring model of adaptive amplitude filtering and gradient enhancement; and S5, performing performance evaluation on the training model in the test stage. According to the method, testing is carried out on an unmanned aerial vehicle data set, the result shows that artifacts are effectively reduced in the aspects of PSNR, SSIM, calculation efficiency and the like, visual consistency is improved, and a new scheme is provided for high-quality blurred image restoration.

The invention relates to the technical field of image optimization recognition, in particular to a deep foundation pit crack detection method and device for deep foundation pit tunneling. According to the method, a deep foundation pit image and a motion vector of an image acquisition device are obtained; and blurred images in the deep foundation pit images are screened out, and rigid edges are identified. And screening out the smear edge by using the position relationship between other edges and the rigid edge, the pixel value change stability on the path and the correlation degree between the edge vector and the motion vector. And constructing a two-dimensional fuzzy kernel, and adaptively setting a fuzzy kernel value by using gradient information and position information of pixel values in the two-dimensional fuzzy kernel. And performing deblurring on the blurred image through the two-dimensional blurring kernel, and performing crack identification. The feature information in the blurred image is extracted and analyzed, the smear effect in the image is quantified, the blurred kernel in the image is determined in a self-adaptive mode, deblurring is carried out, the quality of the deep foundation pit image is improved, and the accuracy of a crack recognition result is improved.

The invention discloses an unmanned aerial vehicle aerial image deblurring method based on an improved DeblurGAN. The method comprises the following steps: firstly, simulating and synthesizing a blurred image data set of unmanned aerial vehicle aerial photography through an Albumentations image data enhancement library; secondly, the data set is preprocessed, image feature information is extracted from a spatial domain and a frequency domain, and feature fusion is carried out; secondly, constructing a dual-branch generative network structure, and enhancing the feature extraction capability of the image and the global attention capability of the image by utilizing the combination of an FPN MobileNet network and a Swin Transformer network; and finally, the training effect is further optimized through the improved loss function, and the recovery effect of the improved algorithm on the aerial blurred image of the unmanned aerial vehicle is evaluated by using the PSNR and the SSIM.

The invention discloses an image deblurring method based on a rotation perception multi-dimensional attention and blurring sensitivity self-adaptive distribution mechanism, aiming at the defects of the prior art in the aspects of complex blurring processing and image reality sense improvement, and belongs to the technical field of image processing, and the method comprises the following steps: preprocessing a collected picture to obtain a training set; training an image deblurring model by using the training set; and performing image deblurring by using the trained image deblurring model. According to the invention, by designing a plurality of innovative modules and combining fuzzy degree adaptive selection, kernel estimation and dynamic weight distribution driven by a physical model, a multi-dimensional rotation perception attention mechanism, spectrum reconstruction anti-noise deblurring and detail enhancement of perception loss optimization, the image deblurring effect and processing efficiency are effectively improved; the method is especially suitable for processing blurred images with high complexity and high resolution.

The invention discloses a flow field measurement system and method fusing event data and a blurred image, and the method comprises the steps: turning on a sheet-shaped light source, and enabling the sheet-shaped light source to illuminate tracer particles in a gas flow field; starting a gas flow field to enable the gas flow field to carry the tracer particles to flow through the transparent box body; capturing the motion change of the tracer particles in real time through an event camera, and collecting event data; blurred images of the tracer particles are synchronously collected through an industrial camera; based on the event data and the blurred image, reconstructing a clear moving image at any moment by using an image deblurring technology EDI; and carrying out time synchronization and space alignment on the clear moving image and the high-resolution static image, and extracting flow field information in the gas flow field in combination with the movement track and brightness change of the particles. According to the method, the high-speed dynamic capture capability of the event camera and the high spatial resolution of the industrial camera are combined, the bottleneck of the spatial-temporal resolution in the traditional flow field measurement technology can be effectively overcome, and meanwhile, the method has a relatively good effect in a complex flow field and a high-speed dynamic scene.

The invention relates to a high-precision three-dimensional Gaussian reconstruction method based on fuzzy perception and joint optimization, and belongs to the field of computer vision and graphic images. The method comprises the following steps: firstly, constructing a fuzzy sensing module, accurately identifying a fuzzy type and an area of an input multi-view fuzzy image, and generating a corresponding fuzzy mask; performing three-dimensional Gaussian representation based on the input multi-view blurred image, estimating internal and external parameters of a camera and sparse point cloud data of a scene, and obtaining initial three-dimensional Gaussian scene representation; and then performing joint optimization on a Gaussian ellipsoid in the initial three-dimensional Gaussian scene representation in combination with an input multi-view blurred image and a blurred mask to generate a clear three-dimensional scene representation. And finally, performing three-dimensional Gaussian reconstruction rendering by using the optimized three-dimensional Gaussian scene representation, and outputting a high-precision three-dimensional Gaussian reconstruction result and a clear rendered image. The method can effectively improve the three-dimensional reconstruction precision and rendering quality under the fuzzy input condition, has high adaptability and stability, and can be applied to the fields of image deblurring, virtual reality and the like.

The invention provides a motion blurred bar code identification method and system based on multi-frame image fusion, and the method comprises the steps: obtaining a continuous image sequence containing a motion blurred bar code, carrying out the motion track feature extraction of the continuous image sequence, and obtaining a motion vector field and a pixel displacement track set of a bar code region in each frame image unit; performing multi-frame image fusion on the continuous image sequence based on the motion vector field and the pixel displacement track set to generate a candidate bar code image set; performing deblurring enhancement processing on the candidate bar code image set to obtain a clear bar code image unit after deblurring processing; and performing bar code area positioning and distortion correction processing on the clear bar code image unit to generate a standardized bar code image, and performing identification to obtain an identification result. According to the invention, the accuracy and reliability of bar code identification in a motion blurred scene can be obviously improved.

The invention relates to the technical field of computer vision, and discloses a robot image recognition system based on a convolutional neural network, and the system comprises a dynamic retina blur sensing module which is used for estimating a pixel-level dynamic blur kernel parameter from an input blur image; the differentiable physical deblurring layer is used for performing feature reconstruction on the blurred image based on the dynamic blurring kernel parameters to generate deblurred image features; and the fuzzy robust CNN backbone network dynamically adjusts the voidage of a convolutional layer according to the dynamic fuzzy kernel parameters so as to adapt to the local fuzzy intensity. The problem of parameter distortion of a data-driven model is solved through an optical flow constraint fuzzy perception module, dilated convolution is dynamically adjusted based on fuzzy intensity to expand a high fuzzy region receptive field, an end-to-end joint training framework is constructed to realize classification error reverse optimization fuzzy kernel parameters, and a two-stage curriculum learning strategy is combined to improve the robustness of the system. The physical model is solidified first and then global optimization is carried out, and the bottleneck of traditional staged training is broken through.

The invention relates to the technical field of underwater image enhancement, in particular to an underwater image enhancement method based on balance, correction and deblurring and application. The balance module is cascaded by color balance and illumination balance, and the color balance performs compensation balance on RGB (Red Green Blue) color channels of the underwater image based on a white balance algorithm of red channel compensation; according to illumination balance, an improved contrast-limited adaptive histogram enhancement method is used for enhancing illumination of a low-illumination area of an underwater image, and meanwhile illumination of a high-illumination area is restrained, so that illumination balance is achieved. In the correction module, a color and illumination composite correction method combining color constancy and an illumination diagram is provided so as to correct the color and illumination of the underwater image at the same time. And in the deblurring module, the image is deblurred by adopting a multi-scale anti-sharpening mask, so that the definition of the underwater image is improved.

The invention provides an inspection vehicle cooperative positioning method and system based on a dynamic fuzzy image, and relates to the technical field of tunnel inspection, and the method comprises the following steps: S1, carrying out slam modeling through a laser radar; s2, the tethered unmanned aerial vehicle performs target detection on the AprilTags codes arranged in the tunnel at equal intervals through a carried RGB camera, and if a first AprilTags code candidate area is obtained through detection, the step S3 is executed; s3, performing deblurring processing on the first AprilTags code candidate region, performing image definition judgment on the processed first AprilTags code candidate region, and if the image is judged to be unclear, turning to step S4; if the image is judged to be clear, turning to step S5; s4, a control instruction is sent to the free unmanned aerial vehicle according to the first AprilTags code candidate area, a second AprilTags code candidate area is obtained, and the step S5 is executed; and S5, positioning according to the processed first AprilTags code candidate area or the processed second AprilTags code candidate area to obtain the position information of the inspection vehicle.

The invention discloses a robot vision external parameter calibration method based on blind deblurring and natural vibration measurement, and the method comprises the steps: collecting a continuous image sequence in a vibration environment through a high-frame-rate camera, extracting pixel-level motion information in combination with an advanced image deblurring deep network and an optical flow feature tracking technology, and carrying out the self-vibration measurement. And further restoring a three-dimensional displacement signal caused by natural vibration of the camera based on a pinhole imaging model, and obtaining ideal position information through frequency domain analysis to realize high-precision visual system external parameter calibration under a vibration condition.

The invention discloses an unmanned aerial vehicle low-altitude photography image enhancement method and system. The method comprises the steps of data preparation, motion joint deblurring optimization, multi-source data registration, scene adaptive enhancement and image comprehensive enhancement. The invention relates to the technical field of unmanned aerial vehicle image enhancement, and realizes high-precision deblurring, precise registration and adaptive visual enhancement of an unmanned aerial vehicle image by fusing inertial measurement unit guided blurring kernel prediction, cross-modal graph structure matching and elastic transformation optimization and an enhancement strategy dynamic adjustment mechanism based on scene analysis. The image definition, the structure consistency and the scene readability are remarkably improved, and the method is suitable for image processing tasks in a complex low-altitude flight environment.

The invention discloses a mold defect visual inspection method, particularly relates to the technical field of industrial machine visual inspection, and is used for solving the technical problem of image spatial variation blurring caused by mechanical vibration under a mobile shooting condition. The method comprises the following steps: acquiring a to-be-detected image on the surface of a mold, analyzing the gradient magnitude of each region, determining the fuzzy characteristics of different regions in the to-be-detected image according to the difference of the gradient magnitudes, evaluating the expected confidence of each region for executing the deblurring operation based on the fuzzy characteristics, and executing the deblurring operation on the regions to obtain a preliminary restored image; an artifact index is calculated in the uniform background area of the preliminary restored image, the distribution concentration degree of image components in each local feature area in the frequency domain is analyzed in the preliminary restored image, and the distribution concentration degree is compared with a preset defect judgment threshold value adjusted according to the artifact index; judging whether the corresponding local feature region is a defect region or not; accurate recognition of mold surface defects under complex imaging conditions is realized.

The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The invention discloses an unmanned aerial vehicle aerial photography fan blade defect detection method based on computer vision. The method comprises the following steps: 1) preprocessing a fan blade image aerial photography by an unmanned aerial vehicle, and inputting the preprocessed fan blade image into a generative adversarial network to carry out deblurring processing; 2) using the fan blade deblurred image processed by the generative adversarial network to form a data set; (3) the YOLOv5s model is improved; 4) training and testing the improved YOLOv5s model by using the data set obtained in the step 2); and 5) detecting the defects of the fan blade through the YOLOv5s improved model. According to the method, the image deblurring method based on the generative adversarial network and the small target detection method based on the improved YOLOv5s are deeply fused, the interference of image blurring on fan blade detection is greatly reduced, and the detection generalization ability of the method in a blurred image environment is effectively improved.

The invention discloses a 5G signal tower three-dimensional rendering and high-altitude panoramic image synthesis method based on GSII, and the method comprises the steps: firstly carrying out the frame extraction processing of data shot when an unmanned plane flies around a signal tower, and obtaining an original image set; obtaining a signal tower sparse point cloud and a camera pose through a motion structure recovery SfM algorithm, and carrying out 3DGS initialization operation; the method is characterized in that in the process of iterative optimization of a three-dimensional Gaussian point cloud, a GSII network is constructed, structural similarity comparison is carried out on an original image of the same visual angle and a deblurred rendering image, a loss function is sensed by using a high receptive field and back propagation is carried out, meanwhile, parameter updating is carried out on a 3D Gaussian ball attribute and FFC residual error repair network, and the quality of a new visual angle rendering image is improved; and finally, designing a central pixel time sequence splicing module, and converting any number of single-view-angle rendering images into a panoramic image. According to the method, the phenomena of artifacts and blurring which are easy to occur under a high-altitude view angle are solved, and the signal tower high-altitude panorama is finally obtained by synthesizing a plurality of rendering images of a single view angle.

The invention relates to the technical field of image deblurring processing, in particular to a deep convolutional neural network-based multi-stage image deblurring method, which comprises the following steps of: designing a multi-scale feature extraction module, a single-scale feature extraction module and a fast codec, and constructing sub-networks with similar structures for respectively extracting more features with different scales; designing a coding and decoding feature fusion module and a fusion strategy which are respectively used for fusing the features of the same sub-network and the features of different sub-networks so as to make up the feature loss caused by image down-sampling, so that the features in different stages can freely flow, and the capturing capability of information of different scales is enhanced; an improved loss function is provided, the structure and details of the image are kept by introducing a content loss function and a frequency reconstruction loss function into a conventional loss function, the high-frequency information of the image is enhanced by the frequency reconstruction loss function, and the visual effect of the image can be improved while the content consistency is kept.

The invention provides a panchromatic sharpening joint optimization method and system based on three-stage progressive fusion. The method comprises three stages of coarse fusion, deblurring detail enhancement and fine fusion. The method comprises the following steps: firstly, performing feature extraction and preliminary fusion on a low-resolution multispectral image and a high-resolution panchromatic image through a dual-path mutual enhancement network; secondly, a multi-layer stacked deblurring module is introduced to perform deep enhancement on fusion features, and the details and definition of the image are effectively improved in combination with partial large kernel convolution, channel mixing and an element-level attention mechanism; and finally, realizing fine optimization of spectrum consistency and a space structure, and outputting a fused image with high spatial resolution and high spectral fidelity. According to the method, the stability and reconstruction quality of panchromatic sharpening under a complex degradation condition are effectively improved through multi-source remote sensing image collaborative modeling and progressive feature fusion, and the method has good practical value and popularization prospect and is superior to a current panchromatic sharpening method based on deep learning.

The invention relates to the related technical field of computer vision, in particular to an event-guided image motion deblurring method in a real scene, which comprises the following steps of: 1, constructing an event-image two-branch fusion deblurring network, and obtaining effective information in a blurred image and an event stream to realize motion blurred image restoration; and 2, in the event branch, converting a spatially sparse event stream into a dense three-dimensional tensor form by using a voxel grid event representation method, and obtaining a spatio-temporal motion feature representation of the event through a spatio-temporal motion enhancement module. According to the event-guided image motion deblurring method in the real scene, an event stream in exposure time is divided into N time slices, the N time slices are respectively accumulated into 2xHxW event frames according to polarities, a 2NxHxW three-dimensional event tensor is constructed, time sequence dependency in 2N event channels is modeled by adopting a bidirectional channel scanning strategy based on a selection state space model, and the time sequence dependency in the 2N event channels is calculated. Information of motion over time over the exposure time is modeled.

The invention relates to the technical field of image processing, and discloses an image deblurring model and method based on double-domain feature fusion, and the method comprises the following steps: collecting data, and building a blurred image data set; building a DAF-UNet neural network based on spatial domain and frequency domain feature fusion; the DAF-UNet neural network is composed of n UNet sub-networks with different scales, and n is a positive integer; based on the blurred image data set, training the DAF-UNet neural network to obtain a trained image deblurring model; collecting a to-be-processed image; and processing a to-be-processed image by using the trained image deblurring model to obtain a clear image result. According to the method, the problems of low information mobility and key feature loss in the prior art are solved, and the method has the characteristics of good image recovery effect and model miniaturization.

The invention discloses a joint injury identification method and system based on arthroscopic imaging. The method comprises the steps that an original image, collected by an arthroscope, of intra-articular tissue is acquired; sequentially performing denoising, artifact removal and deblurring processing on the original image to obtain a preliminary original image; performing corresponding semantic segmentation according to the initial original image so as to segment sub-regions corresponding to different tissues on the initial original image, and performing image enhancement on the different sub-regions by adopting corresponding image enhancement strategies to obtain a target image; and performing visual display on the target image through a display device so as to perform joint injury identification on the current joint. According to the method, arthroscope imaging is enhanced, and medical staff are assisted in recognizing the joint injury state.

The invention discloses a video deblurring method based on 3D space-time grid perception, a medium and equipment. The method comprises the following steps: firstly, receiving a continuous N-frame video image sequence, and taking [N, H, W, 3] as an input dimension vector; the method comprises the following steps: calculating the shake intensity through the optical flow amplitude of adjacent frames, adaptively dividing each frame of image into S * S overlapped grid blocks, and extracting the grid blocks corresponding to pixel positions to form a [N, S, S, 3] sequence; inputting the grid block sequence into a 3D space-time network model for processing, and outputting deblurred grid blocks; and a fusion weight is calculated based on the distance between the pixel and the center of the adjacent grid block, the grid block is weighted and recombined, and a clear current frame image is output. According to the method, efficient and real-time deblurring is realized by utilizing a 3D space-time network and grid block processing in combination with scene shaking characteristics, the problem of blurring of port container monitoring pictures can be effectively solved, and the operation safety is improved.

The invention discloses a video deblurring method based on a deformable space-time sparse converter. The method comprises the following steps: carrying out feature extraction on continuous n frames of fuzzy videos, and calculating forward and backward light streams and corresponding fuzzy images; iteratively updating features of each frame through a bidirectional feature propagation module guided by a multi-scale fuzzy image, and aggregating feature information of different propagation branches; a deformable space-time sparse Transform module is adopted to carry out refinement processing on the aggregation features; and inputting the refined features into a decoder module, and generating a final deblurring result in combination with original input residual connection. The method is suitable for scenes of security monitoring, mobile equipment shooting and the like, the effect of removing the dynamic fuzzy area in the video can be improved, and the overall visual quality of the video is effectively improved.

The embodiment of the invention provides an image processing method, a computer program product, equipment and a storage medium. The method comprises the following steps: acquiring a to-be-processed image, wherein the to-be-processed image is a blurred image with an unknown blurred domain; converting the Y-channel image of the to-be-processed image into a Y-channel image with a known fuzzy domain by using a pre-trained generator of a generative adversarial model, and performing deblurring processing on the Y-channel image with the known fuzzy domain to obtain a clear Y-channel image; and performing enhancement processing on the edge region of the clear Y channel image, and fusing the image obtained by the enhancement processing with the UV channel image of the to-be-processed image to obtain a clear image corresponding to the to-be-processed image. Through the method provided by the invention, a clear image with a clear edge and a good effect can be obtained, and the effect of the clear image obtained through deblurring processing is ensured.

The invention relates to an automatic milling cutter setting method and system based on machine vision, and belongs to the technical field of milling cutter setting. The method comprises the following steps: firstly, positioning initial position coordinates of a milling cutter, and planning an initial tool setting path of the milling cutter by combining target tool setting position coordinates; then obtaining an image of the milling cutter in the initial cutter setting path, carrying out image denoising and image deblurring processing, carrying out edge detection after obtaining a second image, extracting an edge contour of the milling cutter, calculating sub-pixel coordinates of edge points of the contour, and carrying out parametric fitting to obtain a current milling cutter position and a current milling cutter posture; inputting the initial tool setting path, the wear degree of the milling cutter, the current position of the milling cutter and the posture of the milling cutter into an error prediction model to predict the current motion error of the milling cutter; calculating the path compensation amount according to the current motion error of the milling cutter, and adjusting the tool setting path of the milling cutter according to the compensation amount. The method can reduce the interference of the motion blur of the milling cutter and environmental factors, and realizes the quantitative adjustment and correction of the tool setting of the milling cutter.