1456 results about "Image correction" patented technology

The invention discloses an MRI medical image correction method and system based on a convolutional neural network and a computer readable storage medium, and the method comprises the steps: obtaining a brain MRI medical image, and carrying out the preprocessing; extracting lesion features of the brain lacuna infarction from the preprocessed MRI medical image through a convolutional neural network, and positioning a lesion area of the brain lacuna infarction; performing detail enhancement on the lesion area of the cerebral lacuna infarction, segmenting the lesion area and optimizing a segmentation result; and carrying out artifact restoration on the segmented image, and generating a three-dimensional focus visualization model based on the restored image. According to the method, through processing in multiple aspects such as quality evaluation, lesion recognition, segmentation, artifact repair and three-dimensional visualization of the MRI image, the lesion area can be recognized more efficiently and accurately, the image quality is improved, and an efficient and accurate brain lacuna infarction diagnosis auxiliary tool is provided.

The invention discloses a real-time single-stage remote sensing image correction target detection method based on YOLOV8, and relates to the technical field of remote sensing image processing. According to the method, a deformable convolution dynamic prediction local geometric distortion parameter is embedded based on a YOLOv8 backbone network, an adaptive deformation field is generated, pixel-level real-time correction is realized, shallow details and high-level semantic features are fused through a bidirectional path aggregation network, and channel attention and a space gating mechanism are combined, so that the real-time correction of the image is realized. The small target detection capability is enhanced, background noise is suppressed, angle prediction is divided into discrete classification and continuous residual error regression tasks through a decoupling type rotation detection head, angle periodic errors are eliminated in combination with a direction sensitive loss function, and the rotation frame positioning precision is improved. And constructing a dynamic multi-task collaborative loss function, introducing gradient distribution consistency constraint to jointly optimize correction and detection tasks, and realizing feature semantic alignment and model self-enhancement through end-to-end closed-loop training. And the rotating target detection precision and the complex scene robustness are obviously improved.

The invention discloses a global ultra-precision strain measurement method, and relates to the technical field of strain measurement. The method comprises the following steps: continuously shooting moving images of a target area by using a high-frame-rate high-definition camera, and recording accurate time; and carrying out image correction, feature extraction and tracking and data normalization. And after Gaussian white noise is added, singular value decomposition and reconstruction are carried out. And a Lucas-Kanade optical flow method is adopted to calculate the displacement of the feature points in the reconstruction matrix. And a displacement smoothing stage: smoothing the displacement data by using an LOESS method. And carrying out global strain calculation by adopting a window sliding method, and calculating the strain through the spatial derivative of the displacement field. According to the method, the quantization effect can be relieved, the continuity of displacement calculation is improved, low-energy noise and irrelevant features are removed, main motion information is reserved, meanwhile, smoothness is enhanced, local features are captured in a high-precision mode, and measurement sensitivity is improved. The method is suitable for strain measurement of large-scale structures such as bridges and buildings.

The invention discloses a high-precision surveying and mapping method based on surveying and mapping aerial photography, and relates to the technical field of surveying and mapping engineering. S2, selecting aerial photography equipment; s3, making a flight plan; s4, arranging ground control points; s5, aerial photography data acquisition; s6, data preprocessing and image splicing; s7, performing aerial triangulation and model reconstruction; s8, performing multi-sensor data fusion; s9, data analysis and evaluation; and S10, outputting and applying the result. According to the high-precision surveying and mapping method based on surveying and mapping aerial photography, the high-precision real-time image correction and enhancement technology is combined, and the efficiency and precision of a surveying and mapping task can be remarkably improved; the flight path can be quickly optimized through quantum calculation, and the breadth and quality of image acquisition are ensured; real-time image correction and enhancement process image data in the flight process, the workload of post-processing is reduced, and the surveying and mapping process is more intelligent, efficient and flexible.

The invention provides a hull surface defect detection system based on machine vision, and relates to the technical field of data processing. The image correction module is used for carrying out illumination equalization processing and geometric distortion correction; the region construction module is used for identifying a defect-free stable region and generating reference region data which comprises a brightness model and a texture model; the candidate generation module is used for detecting a region where texture interruption or abnormal bright spots exist locally to form candidate defect data, and the candidate defect data comprise pixel positions and local contrast parameters; the stability judgment module is used for carrying out projection matching in the multiple frames of images and simultaneously carrying out joint comparison with the brightness model and the texture model of the reference area data to form real defect data and false defect data; the result output module is used for generating a detection result containing defect coordinates, defect contours, image frame numbers and interference sample prompts; the accuracy of hull surface defect detection is improved.

The invention provides a geometric correction method and device for a satellite remote sensing image and a storage medium, and relates to the technical field of image correction, and the method comprises the steps: obtaining remote sensing image data of at least two different spectral bands, synchronously recording satellite attitude parameters and orbit position parameters, and carrying out the verification processing; based on the satellite attitude parameters and the orbit position, geometric coordinate conversion is carried out on each remote sensing image data, and target remote sensing image data is generated; dividing each piece of target remote sensing image data into a plurality of continuous grid units, and constructing a local geometric transformation model at the vertex of each grid unit; and resampling the original pixels in each grid unit according to the local geometric transformation model, and outputting a plurality of pieces of target remote sensing image data subjected to geometric correction. According to the invention, the geometric correction precision of the high-resolution satellite remote sensing image can be improved.

The invention discloses a drug traceability code acquisition method, system and device based on image recognition, and a medium. The method comprises the steps of obtaining a drug package image; preprocessing the medicine packaging image, and performing edge detection, region segmentation, image correction and barcode analysis on the preprocessed medicine packaging image to obtain medicine traceability code information; obtaining prescription data, and checking the drug traceability code information according to the prescription data; and when the drug traceability code information is checked to be correct, carrying out associative storage on the drug traceability code information and the prescription data. According to the invention, drug traceability code information can be rapidly and accurately identified, and environments such as complex backgrounds, illumination changes, traceability code deformation and the like can be adapted; meanwhile, multiple drug traceability codes can be collected, the purpose of batch collection is achieved, supervision of a drug dispensing end is achieved, medication safety of patients is ensured, drug management can be perfected, and subsequent drug traceability is facilitated.

The invention discloses a strawberry internal quality and maturity nondestructive testing method based on an MPCD-Mama model. The strawberry internal quality and maturity nondestructive testing method comprises the following steps: acquiring a strawberry sample, acquiring a hyperspectral image by using a hyperspectral reflection imaging system, measuring SSC, PH and hardness, and marking maturity; correcting the hyperspectral image, performing spectrum extraction to form a data set, and dividing the data set into a training set, a test set and a verification set; the method comprises the following steps: constructing an MPCD-Mama model, training the MPCD-Mama model by using a training set, testing the performance of the MPCD-Mama model by using a verification set on the basis of a cross validation method, predicting related indexes on the basis of the pre-trained MPCD-Mama model, generating heat maps of corresponding components at different positions of strawberries, and displaying the distribution conditions of the different components. According to the method, nondestructive testing of multiple quality parameters of strawberries is achieved, compared with a traditional method, the prediction precision is remarkably improved, and technical support is provided for nondestructive testing of the quality of the strawberries.

The invention relates to the technical field of environment assessment, in particular to a coastal zone culture area multi-dimensional environment assessment method and a cloud platform. The method comprises the following steps: continuously recording dissolved oxygen concentration, pH value, centigrade temperature, salinity unit, turbidity scattering unit, chlorophyll fluorescence intensity, ammonia nitrogen milligram per liter and nitrite milligram per liter, synchronously calling multi-source remote sensing images covering a culture area and an adjacent water area, extracting remote sensing reflectivity data through image correction and atmospheric correction, and calculating to obtain water color parameters. According to the method, the water quality sensor array is deployed at the key point of the culture area, continuous multi-parameter environmental data acquisition is carried out, multi-source remote sensing images are synchronously integrated, water color information is extracted in real time, and the timeliness and data accuracy of culture water area environmental monitoring are improved; trend decomposition and dynamic baseline construction are carried out based on the data sequence, so that the environmental fluctuation evaluation of the breeding area is more objective and accurate.

The invention relates to the technical field of image processing and computer vision, in particular to a digital human image correction method, electronic equipment and a storage medium. The digital human image correction method comprises the following steps: preprocessing an image; extracting features; deviation analysis; generating a correction strategy; and correcting the image. According to the invention, image correction is realized by using an image processing library, a deep learning framework and the like through related software modules, so that the digital human image quality is improved, and the sense of reality and the visual effect are enhanced.

The invention discloses a monocular and binocular cooperative localization and mapping method and device for underwater refraction compensation, and the method comprises the steps: extracting checkerboard angular points, compensating an underwater light path through employing a two-time vector correction model based on a Snell law, and precisely calibrating the internal and external parameters of a binocular camera. According to the method, an underwater real light path is effectively restored, and calibration errors caused by medium refraction are remarkably reduced. Then epipolar correction is performed on the corrected left and right images, so that the geometric consistency of stereo matching is ensured; and finally, monocular and binocular poses are fused, and seven-degree-of-freedom similarity transformation global alignment is applied, so that the problem of monocular scale uncertainty is effectively solved, and high-precision and high-robustness three-dimensional mapping is realized by means of binocular real scale introduction. Key links such as camera pre-calibration, refraction compensation, image correction, monocular tracking, binocular depth measurement, scale alignment and the like are connected in series to form an integrated process, so that the underwater vision positioning and mapping precision is improved.

The invention relates to the technical field of machine vision, and discloses a side slope anchor rod drilling platform construction quality intelligent detection system based on machine vision. The method comprises the steps that an acquisition module acquires a multi-angle original image with a space-time mark; the correction module performs preprocessing to obtain a standardized image; the extraction module constructs a drilling platform three-dimensional model; the monitoring module detects track deviation by using an improved adaptive Kalman filtering algorithm; the calculation module evaluates the drilling quality; and the analysis module comprehensively analyzes and generates quality grade and early warning information. According to the intelligent detection method for the construction quality of the side slope anchor rod drilling platform based on machine vision, real-time monitoring, dynamic analysis and quality early warning of the construction process are achieved, subjectivity and hysteresis of traditional manual detection are overcome, and the detection precision and efficiency of the anchor rod drilling construction quality are improved.

The invention discloses an image data processing method, image acquisition equipment and a storage medium, and relates to the technical field of image correction, and the image data processing method comprises the following steps: determining an inertial state vector of the image acquisition equipment at an image acquisition moment according to inertial sensing data, and determining an inertial state vector of the image acquisition equipment at an image acquisition moment according to image visual information and the inertial state vector; determining a target state vector; updating a current process noise covariance according to the error variation of the inertial state vector and the target state vector to obtain a target process noise covariance; and based on the target process noise covariance and a preset Kalman filtering algorithm, updating the target state vector at the next moment corresponding to the image acquisition moment. Through dynamic updating of the process noise covariance during Kalman filtering processing, the dynamic change of the sensor error is reflected in real time based on the dynamically updated noise covariance, and the image correction effect is improved.

The invention relates to the technical field of image correction, in particular to a correction method and system for a low-distortion image of a panoramic camera. The method comprises the following steps: based on a second-hand vehicle scene acquisition demand, acquiring a panoramic image and executing basic preprocessing to obtain a preprocessed panoramic image; calling a lens distortion coefficient, analyzing a corresponding distortion parameter in combination with a checkerboard calibration algorithm, and generating a polar region distortion distribution diagram; carrying out segmentation and grid reconstruction on a polar region of the preprocessed panoramic image, and executing distortion elimination and geometric reduction to obtain a preliminary correction image; fine tuning is carried out on the pixel position of the preliminary correction image, color fault and edge dislocation of a multi-lens splicing seam are eliminated, and an optimized correction image is obtained; executing second-hand car scene customized enhancement processing on the optimized correction image to obtain an enhanced correction image; and automatically checking the enhanced correction image, and outputting a low-distortion final correction image. According to the invention, the correction efficiency of the panoramic low-distortion image of the second-hand vehicle can be improved.

The present invention provides a method, system, device and medium for automatically cataloging RQD of drilling cores, which belongs to the field of underground engineering drilling engineering informatization. The system of the present invention includes an image acquisition module, an image preprocessing module, an RQD calculation module and a cataloging module. The present invention collects core box images in batches in a standardized manner, uses a semantic segmentation network and a projection transformation to correct the distortion of the core box images, and then uses semantic segmentation to mark core blocks of 10CM and above, sorts the recognition results and calculates the RQD; uploads the on-site collected images to the cloud, and integrates the core box image correction and RQD calculation algorithm in the cloud, and performs image data storage, RQD calculation and RQD database cataloging in the cloud to achieve digitalized and standardized data backup. The technical solution of the invention can efficiently and standardizedly digitize and store core information, reduce the cost of mine data storage, improve data utilization, and provide data basis for mine rock mass quality evaluation.

The invention relates to the technical field of underwater sediment measurement, in particular to a microspur measurement device and method, and provides the following scheme: firstly, screening out an initial local selected area through overall image graying and feature extraction, and adjusting the local area through iteration to ensure that a microspur image can accurately reflect particle features in the overall image; then, decomposing and extracting high-frequency edge information by using a Laplacian pyramid, eliminating background noise through gray variance, and generating a particle region graph; further separating adjacent particles by using an adhesive particle cutting technology, and finally generating a high-quality sediment image through image correction; according to the method, the microspur image acquisition frequency can be reduced, the particle measurement accuracy is improved, the data processing complexity is reduced, and the method has relatively high measurement precision and practical value.

The invention belongs to the technical field of image processing, and particularly relates to a cable shielding layer weaving density detection system based on image analysis, which comprises an image correction module, a frequency domain analysis module, a self-adaptive filtering module and a density defect detection module, obtaining the maximum peak point of the centralized complex frequency spectrum; constructing a band elimination filter according to the maximum peak point, filtering the centralized complex frequency spectrum through the band elimination filter to obtain a filtered complex frequency spectrum, executing inverse Fourier transform to obtain a filtered space image, and binarizing the filtered space image through a threshold segmentation algorithm to obtain a pore mask; the porosity is obtained according to the ratio of the non-pore pixel points to the total number of the pixel points; and judging the weaving defect of the shielding layer according to the porosity. According to the method, the strong periodic signals of the weaving textures are automatically identified and filtered in the frequency domain, so that non-periodic defects such as pores are effectively highlighted, the technical problem that periodic textures cover defect signals is solved, and the accuracy of defect detection is improved.

The invention provides an image correction method and system based on a nonlinear distortion distribution elliptical fisheye lens, and a medium. The method comprises the following steps: establishing a nonlinear mixed distortion imaging model based on a mapping relation between image coordinates and a spherical direction; defining a distortion vector, and performing fitting processing on the distortion vector based on a two-dimensional B-spline function to obtain a distortion function; the method comprises the following steps: acquiring images in a five-surface three-dimensional angle calibration space based on an elliptical fisheye lens, analyzing lens imaging parameters, and generating parameter values of a distortion function; analyzing parameter values of the distortion function based on a nonlinear mixed distortion imaging model to generate calibration parameters, and remapping pixel points in the distorted image based on the calibration parameters; performing image reconstruction on the remapped distorted image to obtain a corrected image; improving the sampling density of a target area by constructing a nonlinear mixed distortion imaging model; the system adapts to complex monitoring environments such as galleries and entrances and exits; and spatial distortion is flexibly expressed through a B spline, so that the modeling capability is improved.

The embodiment of the invention provides an image processing method and device, a storage medium and a program product. According to the embodiment of the invention, the method comprises the steps: obtaining a target image with an initial pose, converting the target image into point cloud data according to the depth information of the target image, and carrying out the plane segmentation based on a normal vector of the point cloud data, so as to recognize at least one target plane; if the number of the target planes reaches or exceeds a set number threshold value, extracting at least one group of plane combinations formed by three mutually vertical target planes from the planes, and determining a correction pose of the target image through a normal vector matrix of the combinations; if the number of the target planes is insufficient, extracting a reference plane from the point cloud data, and determining a correction pose according to a normal vector; and finally, performing vertical correction on the target image according to the correction pose, thereby improving the accuracy and robustness of target image correction, simplifying the operation process, reducing manual intervention, and improving the three-dimensional reconstruction quality or the VR display effect.

The invention discloses a wide-angle camera distorted image correction method and device based on track visual detection. The method comprises the following steps: acquiring a track distorted image; the projection imaging intersection point between the sleeper and the steel rail is adopted to form a mark point; solving the distortion center of the camera by combining a distortion model according to the mapping relation between the three-dimensional world coordinate and the projection coordinate of the three-dimensional world coordinate in the distorted image; solving distortion parameters of a camera by combining a Levenberg-Marquardt algorithm according to the fact that blanking points formed by parallel steel rails in the image must be subjected to collinear constraint; re-projection correction is carried out on the mark points participating in calculation; screening out bad mark points and removing the bad mark points; the distortion center and the distortion parameters are calculated again until all the mark points meet the 3 sigma criterion; and according to the optimal distortion center and the distortion parameters, track image correction is carried out through a distortion model. According to the invention, high-efficiency and accurate distortion correction can be carried out on line without the participation of a special cooperative calibration object.

The invention relates to an image correction technology, and discloses a CBCT (cone beam computed tomography) image osteosclerosis artifact correction method, which comprises the following steps: acquiring a high-energy projection image and a low-energy projection image of a target object, performing image reconstruction, and calculating to obtain a primary artifact-free image; extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, calculating a bone weight, taking the artifact image of the target object as training input data, taking the bone image and the soft tissue image as reference data, and training to obtain a dual-channel artifact correction model; and outputting a preprocessed bone image and a preprocessed soft tissue image according to the to-be-processed osteosclerosis artifact image by using a dual-channel artifact correction model, and carrying out weighted fusion on the preprocessed bone image and the preprocessed soft tissue image according to a bone weight to obtain an artifact-free reconstructed image. The invention further provides a CBCT image osteosclerosis artifact correction device, electronic equipment and a storage medium. According to the invention, the image artifact correction effect can be improved.

The invention discloses a vascular intervention operation robot control method and system. The method comprises the following steps: processing an angiography image; comprising the steps of image enhancement, image segmentation and image correction after segmentation, carrying out feature extraction on a processed image, obtaining a blood vessel state index value by combining blood vessel image features and other information of a patient, obtaining a guide wire control parameter according to the blood vessel state index value, and correcting the control parameter. And controlling the vascular interventional operation robot based on the corrected PID control parameter value. The control parameters of the guide wire are adjusted according to different blood vessel states, so that the advancing control of the guide wire is matched with the blood vessel states, and the entering safety and the advancing efficiency of the guide wire are ensured.

The invention relates to the technical field of image correction, in particular to an error calibration method and system for a three-dimensional laser scanner, and the method comprises the steps: obtaining three-dimensional point cloud data in each scanning direction through the three-dimensional laser scanner; any three-dimensional point cloud number is subjected to pooling processing, a plurality of boundary feature curves in pooled point cloud data are obtained, the pooled point cloud data are screened to obtain target point cloud data, reference point clouds are further screened out, the positions of feature points in the reference point clouds are adjusted, adjacent feature points are combined to obtain an adjustment trend consistent area, and the adjustment trend consistent area is obtained; and carrying out movement adjustment on data points except the feature points in the reference point clouds by utilizing the overall adjustment condition of the feature points in the adjustment trend consistent region, carrying out position adjustment on all the data points in all the reference point clouds to obtain new reference point clouds, and carrying out registration splicing on all the new reference point clouds. According to the invention, the accuracy of the three-dimensional point cloud data collected by the three-dimensional laser scanner is improved.

The invention discloses a fabric apparent morphology three-dimensional reconstruction method under multi-view progressive matching. The method comprises the following steps: (1) building a closed shooting device; (2) acquiring a fabric image; (3) image preprocessing; (4) camera calibration and image correction: carrying out parameter calibration on the camera of the shooting device by adopting a checkerboard calibration method to obtain internal and external parameters of the camera, and carrying out correction operation on the acquired fabric image; (5) feature extraction and stereo matching: extracting appropriate feature points from the obtained left and right views of the fabric, and carrying out feature matching on the two images to obtain an optimal matching pixel pair; (6) generating a disparity map: calculating the fabric disparity map by utilizing the position difference between the left and right view pixels in the optimal matching pixel pair set; and (7) point cloud generation: calculating a depth map of each pixel value according to the view difference obtained in the step (6) so as to obtain a depth map of the fabric surface morphology and form a three-dimensional point cloud of the fabric apparent morphology.

The invention relates to the technical field of image processing, and discloses an image correction method and system for binocular diffraction AR intelligent glasses. The method comprises the following steps: calculating a dispersion response parameter of a diffractive optical element in the AR glasses in a visible light wave band; respectively carrying out dispersion distortion detection on the left eye image and the right eye image in the AR glasses to obtain a left eye dispersion distortion feature and a right eye dispersion distortion feature; interpupillary distance change data of a user is obtained, effective aperture offset calculation is carried out on the diffractive optical element, and interpupillary distance compensation parameters are obtained; performing coupling calculation on the left eye dispersion distortion feature, the right eye dispersion distortion feature and the interpupillary distance compensation parameter to obtain a binocular dispersion correction matrix; and performing dispersion compensation on the left eye image and the right eye image to obtain a dispersion correction image, and outputting the dispersion correction image to a display module of the AR glasses. The method can respond to the pupil distance change of the user in real time, automatically adjust dispersion correction parameters, realize accurate and comprehensive dispersion correction, and ensure the stability of dispersion correction performance.

The invention discloses a confocal microscopic image distortion correction method based on a two-axis MEMS (Micro Electro Mechanical System) scanning galvanometer. The method comprises the following steps: acquiring an original image by using a confocal microscope of a two-axis MEMS scanning galvanometer; calculating to obtain a galvanometer normal vector; calculating to obtain an incident light unit vector; calculating to obtain a reflected light unit vector; according to the method, distortion introduced by the MEMS galvanometer can be removed, distortion introduced by the MEMS galvanometer can be corrected under the condition that the posture of the scanned end face is unknown, and then a distortionless confocal image is obtained.

The invention discloses a farmland soil moisture content real-time surveying system and method based on unmanned aerial vehicle multispectral imaging. The method comprises the steps that multispectral sensor parameters are configured, and an unmanned aerial vehicle flight scanning path is planned; a space coordinate system and time synchronization mechanism is established, and high-precision positioning and time synchronization are achieved; collecting and calibrating visible light, near-infrared and short-wave infrared original image data to form a spectral signal set; calculating an image signal-to-noise ratio, screening a low-quality image, and executing noise suppression; extracting timestamp information from the suppressed image, performing spatial positioning, and establishing a spatial corresponding relation database; calculating time synchronization precision and performing image correction; evaluating the spatial resolution according to the distortion coefficient; calculating a comprehensive quality parameter, comparing the comprehensive quality parameter with a threshold value, and obtaining a soil moisture content distribution parameter when a condition is met; a farmland moisture state map is generated, partition marking is carried out according to a multi-level standard, a monitoring result is output, and high-precision and real-time monitoring of the farmland soil moisture content is achieved.

The invention discloses a defect detection method and defect detection equipment for a circular printed matter. The method comprises the following steps: automatically extracting a minimum enclosing rectangle from a sample circular printed matter area to obtain an image correction proportion; fitting a circular contour of the circular printed matter in the sample circular printed matter area after the proportion correction, randomly selecting two matching areas in an image area enclosed by the circular contour, and performing Mark point positioning based on the two matching areas; acquiring a to-be-detected circular printed matter image, performing proportion correction on the to-be-detected circular printed matter image according to the image correction proportion, and selecting two matching areas to perform Mark point positioning; inversely transforming a Mark point positioning result into the to-be-detected circular printed matter image after scale correction; and aligning the to-be-detected circular printed matter image with the sample circular printed matter image, and carrying out defect detection by adopting a difference image method. The automatic detection precision and the detection efficiency of the circular printed matter can be improved.

The invention discloses a deep learning-based mixed-shooting image correction method and system, and relates to the field of computer vision, and the method comprises the steps: carrying out the illumination normalization processing of a mixed-shooting image group, and constructing a Gaussian pyramid image group; inputting the Gaussian pyramid image group into a ResNet-34 network to extract a multi-scale feature map, and generating spatial position and channel statistical information; and converting the spatial position and the channel statistical information into a DNA sequence fragment through a DNA base mapping rule. Illumination normalization is achieved through Retinex decomposition and Gaussian filtering, the exposure difference of different devices is effectively eliminated, the input quality of follow-up processing is guaranteed, traditional feature description is converted into biological sequence comparison through DNA base coding, geometric parameters are optimized in combination with quantum annealing, and the accuracy of the method is improved. The matching precision of a low-texture area and the correction effect of a large-view-angle-difference scene are remarkably improved, and the sub-pixel-level matching precision is achieved based on weighted RANSAC and multi-scale pyramid fusion.

The invention discloses a road obstacle recognition system based on image processing, which relates to the field of road obstacle recognition systems and comprises a camera group of an image acquisition module, an image preprocessing module, a built-in processor, an algorithm denoising module, a stretching comparison module, a data processing module and a data processing module, the method comprises the following steps: combining a vehicle data correction image, a feature extraction module, a leading-edge CNN variant model, sample training, feature extraction by means of a technology, accurate target feature capture, an obstacle identification and classification module, and a vehicle and target dynamic, trajectory prediction, decision information providing, warning and feedback module based on the extracted features by using an integrated learning classifier. A flexible screen is arranged to display information in real time, sound and light alarms are graded according to risks, danger instructions are quickly transmitted to an ECU, and the vehicle is driven to operate emergently. The system has remarkable advantages, image acquisition and preprocessing adapt to the environment, feature extraction, recognition and classification are accurate and efficient, and driving safety is guaranteed.