476 results about "Gradient direction" patented technology

The invention relates to the technical field of image detection, in particular to a PCBA board defect detection method and system based on image processing, and the method comprises the following steps: carrying out the meshing calculation of a gray scale deviation after a gray scale image is subjected to Gaussian filtering denoising, generating change rate data, carrying out the statistics of a frequency number, constructing a histogram, combining with an Otsu algorithm, and generating a candidate mask; extracting pixels based on a mask, calculating a gradient modulus, screening edge candidate points, carrying out gradient direction connection and morphological processing to generate a complete edge structure, expanding a connected domain through a region growing algorithm, aligning the connected domain with a template contour, and outputting defect coordinates. According to the method, the defect identification sensitivity is improved through combination of gray level image gridding processing and dynamic threshold calculation, a candidate mask is generated through grid gray level change rate statistics and an Otsu algorithm to avoid over-segmentation missing detection, and the contour precision is improved through combination of gradient modulus difference screening and morphological closed operation optimization. The region growing algorithm and template dynamic alignment reduce deformation misjudgment, and staged dimension reduction and feature enhancement reduce calculation complexity and solve resource waste.

The invention relates to the field of resistor defect detection, in particular to an alloy resistor surface defect real-time detection method and system based on image processing. The method comprises the following steps: acquiring an alloy resistor surface image, calculating a local sudden disturbance factor of a pixel point, analyzing a gray offset condition and a gradient direction deflection condition in a neighborhood of the pixel point, and calculating a gray texture disturbance factor; calculating a local defect response factor; obtaining each candidate region, analyzing the shape of each candidate region, and constructing a salient region structure responsivity in combination with local defect influence factors of pixel points in the candidate regions; giving a suspected abnormal weight to each pixel point in the gray scale resistor surface image, constructing a weighted gray scale histogram based on the suspected abnormal weight and the gray scale value, obtaining a segmentation threshold in the weighted gray scale histogram by using an Otsu threshold segmentation algorithm, and detecting the surface defect of the alloy resistor; and the precision of alloy resistor surface defect detection is improved.

The invention provides an aluminum profile defect analysis method and system based on texture features, and relates to the technical field of edge detection, and the method comprises the steps: obtaining an aluminum profile surface image, and determining an extrusion direction; dividing the image into a plurality of local blocks, carrying out gradient direction and amplitude calculation on each block, and judging whether the block belongs to a high-confidence-coefficient texture region or not by combining a direction difference value and confidence coefficient; a first suppression coefficient is executed on the high-confidence-coefficient texture region for primary reduction to form a first processing image, then the updated image is judged again, a second suppression coefficient with higher strength is applied to the region still having obvious texture features, and a second processing image is generated; and finally, defect identification is carried out on the weakened image through edge detection, and residual texture false edges are removed in combination with direction consistency or connectivity analysis. The method has the advantages of light weight, low computing power consumption and high accuracy, and can be applied to online detection and quality control of the surface flaws of the aluminum profile in industrial production.

The invention relates to the technical field of electronic component detection, in particular to an IC (integrated circuit) carrier plate detection method based on surface state image extraction, which comprises the following steps: acquiring a gray image, analyzing structural parameters, extracting gradient features, detecting boundary disturbance, integrating the image, calculating an abnormal score, identifying a defect position area, extracting features and outputting an identification result. According to the invention, by analyzing the structure parameters of the bonding pad in the gray level image, calculating the edge line segment, the center coordinate and the spacing, and constructing the two-dimensional coordinate system, the regional positioning reference is enabled to have geometric consistency, the coordinate mapping is combined with the gradient direction change frequency and the continuous aggregation point, the boundary disturbance identification precision is improved, and the image division is executed based on the disturbance region. According to the method, non-functional region mixing is effectively avoided, a clustering and probability model is introduced after region gray level statistics, a deviation scoring mechanism is constructed, gray level feature abnormity is accurately recognized, the discrimination capability of small-amplitude and low-contrast defects is improved, and the selectivity and target focusing performance of feature detection are enhanced.

The invention provides an evidence obtaining method and system based on image processing, and the method comprises the following steps: S1, generating a pixel-level depth-of-field distribution diagram of an input image through a multi-scale encoder-decoder network, employing an edge perception optimization layer in a decoding stage, and improving the depth-of-field boundary precision through minimizing a local gradient consistency loss function; s2, performing depth-of-field rationality verification based on an optical imaging physical model, and triggering a first-level tampering alarm by calculating a defocusing fuzzy radius and a gradient direction of a selected region when a difference between the defocusing gradient directions of a target region and a background region exceeds a preset threshold value; and S3, dynamically positioning a key pixel region, identifying a depth-of-field mutation boundary by using an edge detector, calculating by combining local texture complexity, screening a pixel set of which the entropy value is higher than a threshold value and which is located at the mutation boundary, correlating metadata to verify the rationality of the physical size and the spatial position of an object, and eliminating false detection caused by perspective transformation.

The invention belongs to the field of cable eccentricity detection, and particularly relates to a cable eccentricity detection method and system based on X rays. The method comprises the following steps: calculating a gradient magnitude diagram and a gradient direction diagram through an X-ray image of a cable, screening a point with the local maximum gradient magnitude and low neighborhood divergence as a contour starting point, performing contour tracking to generate a contour point set, and selecting a next contour point based on a tangential prediction direction; performing ellipse fitting on the contour point set to obtain a geometric center, long and short axis parameters and a root-mean-square error of a fitting ellipse, dividing the fitting ellipse into an inner candidate ellipse and an outer candidate ellipse according to a long axis, and matching the inner candidate ellipse meeting the condition for each outer candidate ellipse; and screening out an outer candidate ellipse and an inner candidate ellipse which meet conditions from the candidate pairs, and calculating the eccentricity of the cable to be measured based on geometric center coordinates of the outer candidate ellipse and the inner candidate ellipse. According to the invention, the accuracy and reliability of cable eccentricity measurement results can be improved.

The invention relates to the technical field of multi-source data fusion, in particular to a multi-source geological data processing method and system of a three-dimensional geological model.The method comprises the following steps that mountain landform and river valley images are obtained, gray frequency characteristics are extracted, a frequency energy gradient layer is constructed, a frequency continuous response area is screened to generate a structure boundary set, and a structure boundary set is constructed; the method comprises the following steps of: extracting a boundary normal vector by utilizing principal component analysis, identifying boundary sections with consistent directions, estimating a physical property parameter gradient direction, judging an included angle screening blocking region, generating a space attribute limiting layer, carrying out space alignment analysis on an overlapping region vector included angle, updating a boundary label, and generating an available attribute path structure set in three-dimensional geological modeling through a Dijkstra algorithm. According to the method, a conduction model is constructed through frequency domain decomposition and logarithmic transformation enhanced recognition, frequency window analysis noise reduction, principal component extraction vector analysis direction and center difference estimation, dynamic matching is promoted through alignment, a Dijkstra algorithm optimizes a path, and the geological model bedding characterization and conduction simulation precision is improved through cooperation of a multi-dimensional technology.

The invention discloses a urinary calculus image recognition and analysis system based on deep learning, which relates to the technical field of urinary calculus image recognition and comprises a structure communication mapping module, an edge disturbance analysis module, a structure fidelity coding module, a trusted path assignment module and a self-adaptive screening regulation and control module. The edge disturbance analysis module is used for constructing an edge direction difference matrix based on the region connectivity vector, carrying out gradient direction analysis on boundary pixels in the enhanced image and extracting edge frequency disturbance characteristics; and the structure fidelity coding module is used for correspondingly fusing the region connectivity vector and the edge frequency disturbance characteristics according to position indexes, constructing a structure integrity description vector and constructing a structure fidelity kernel function based on the structure integrity description vector so as to generate a structure fidelity score. According to the method, the problem of training misleading caused by incomplete enhanced image structure is solved, structural integrity screening and path optimization of training samples are realized, and the model recognition precision and stability are improved.

The invention relates to the field of image recognition, in particular to an intelligent weld defect detection method based on machine vision, and the method comprises the steps: carrying out the collection and feature preparation of a weld region image, and obtaining a pixel point basic gray feature data set; performing trend prediction comparison on the local gray profile of the pixel point to obtain the deviation degree of the local gray profile; performing unit vector aggregation analysis on a pixel point neighborhood gradient direction to obtain a local gradient structure disorder degree; multiplicative modulation is carried out on the deviation degree of the local gray profile and the disorder degree of the local gradient structure to obtain a distance measurement function of structure perception; a weld defect recognition result is obtained by performing clustering analysis on a distance metric function of structure perception, so that the problem of missing detection caused by the fact that benign heterogeneous points and malignant defect points cannot be distinguished by the Euclidean distance in existing weld defect detection is solved.

The invention relates to the technical field of pattern classification, in particular to a pattern classification method and system based on a regional label, and the method comprises the following steps: extracting a local label threshold based on a pixel gray range, analyzing the regional difference of a pattern image, screening a change optimal group as a reference, analyzing a gradient direction and amplitude mutation, and screening layered breakpoints. And integrating the regional features, and judging category attribution to obtain a discriminant quantity. According to the method, detailed pixel relation analysis and regional structure feature extraction are executed step by step, label affiliation accurate adjustment is achieved based on local statistics and spatial attribute association synchronization, boundary anomaly is judged through gradient and label change multiple parameters, layering is carried out, nodes are automatically positioned according to attribute mutation, and category affiliation is decided by multi-dimensional attribute interaction. Layered and partitioned analysis of high-detail and complex patterns is realized, hierarchical attribution requirements under multiple scenes are adapted, the region classification accuracy is enhanced, and the response capability to the internal differential and hierarchical relationship of the pattern structure is improved.

The invention relates to a machine vision-based meshing precision measurement calibration method for a gear transmission system, and belongs to the technical field of calibration workpieces. The method comprises the following steps: extracting a gradient direction of a contact edge point in an image, analyzing a direction change trend, removing a mutation point, and constructing an edge direction paragraph sequence; positioning a tooth profile contour intersection point according to the sequence, analyzing longitudinal coordinate fluctuation, and identifying an axial dislocation structure; comparing the actual tooth crest contour with a standard path, extracting the direction and length of a difference section, and generating an adjustment direction mark group; converting the direction mark into a fine adjustment angle sequence, collecting an image and identifying a direction stable section; and finally, dividing image sub-blocks, analyzing the curvature direction of a spliced boundary, identifying curvature mutation points and establishing a calibration scheme. According to the method, the integrity of gear meshing area structure recognition and the continuity of an image sequence are effectively improved, high-sensitivity axial dislocation detection and accurate direction guiding fine adjustment are achieved, the curvature sudden change recognition and multi-dimensional error response capability is enhanced, the engineering practicability and the automation level are high, and the method is suitable for large-scale popularization and application. The method is suitable for metering calibration and quality control of a high-precision gear transmission system.

The invention relates to the technical field of computer vision and power equipment detection, in particular to a power transmission line conductor sag intelligent judgment method based on an unmanned aerial vehicle inspection image, which comprises the following steps: S1, identifying a conductor area based on an unmanned aerial vehicle visible light image, and extracting a continuous conductor edge; s2, an insulator string is positioned in a wire area, and a wire hanging point is accurately positioned through gradient direction aggregation analysis; s3, generating a space datum line based on the digital elevation model and the line direction; s4, conducting wire form reconstruction of physical constraint is executed, the fracture edge is connected with the physical characteristics of a conducting wire catenary as constraint, and the reconstructed conducting wire is projected to a datum line vertical plane; and S5, calculating the maximum vertical distance from the projection point to the reference line as a sag value, and dynamically diagnosing abnormality in combination with the environment temperature. Through combination of image processing, deep learning, a physical model and a terrain adaptive technology, the precision and reliability of conductor sag detection can be effectively improved.

The invention discloses a multi-modal learning method and device under different perception sources, equipment and a medium, and relates to the technical field of machine learning. Semantic energy scores of different modals are adjusted by utilizing learnable temperature parameters, and interaction features are weighted by utilizing semantic energy weight scores, so that the learning efficiency is improved. In the process, semantic quality of each mode is dynamically evaluated through an energy score, a noise mode is suppressed, key information is highlighted, and a temperature regulation and dynamic gating mechanism is introduced to eliminate quality difference and semantic asymmetry between the modes and realize adaptive feature fusion; then, a gradient adjustment factor is obtained according to the confidence coefficient ratio, a direction consistency adjustment factor is generated according to cosine similarity, and in the process, gradient scores represented by the gradient adjustment factor obtained based on the confidence coefficient ratio are sensed; and the gradient direction represented by the direction consistency adjustment factor generated by the cosine similarity is aligned with two dimensions to adjust the modal gradient, so that the overall performance of multi-modal learning is finally improved.

The invention belongs to the technical field of image processing, and particularly relates to an aluminum bar quality detection method and system based on machine vision, and the method comprises the steps: obtaining a gray image of the surface of an aluminum bar; constructing a structure tensor for describing the distribution of the pixel points in the local gradient direction, and obtaining gradient coherence indexes of the pixel points according to feature values of the structure tensor; according to the gradient coherence index, the gray value of the pixel point and the gray value mean value and the gray value standard deviation in the neighborhood, obtaining coherence weighted saliency of the pixel point; according to the coherence weighted saliency and the coherence weighted saliency of all the pixel points in the pixel point neighborhood, acquiring a defect probability index of the pixel points; and performing aluminum bar surface quality discrimination according to the defect probability index. According to the method, through combination of gradient coherence and local brightness statistical characteristics, interference of specular reflection light spots on the surface of the aluminum bar and wiredrawing texture noise is effectively inhibited, and accuracy of detection of weak defects such as scratches is improved.

The invention relates to the technical field of image recognition, in particular to a shearing behavior dynamic correction method and system based on wear state recognition. Acquiring a digital image sequence of the cutting edge area; constructing an image feature separation network, and performing parallel feature extraction on the preprocessed digital image sequence; identifying a pixel-level high-frequency texture discontinuous region and an edge gradient direction field by utilizing continuity characteristics of a cutting edge surface periodic texture mode to obtain a target defect probability graph; identifying a projection shadow area and a low-frequency illumination halation of the edge of the bulge by using a backlighting imaging model to obtain an interference artifact probability graph; establishing spatial mutual exclusion constraints of the target defect probability graph and the interference artifact probability graph in a pixel space, and generating a defect binary mask; performing multi-dimensional texture feature calculation on an area corresponding to the defect binary mask, and constructing a surface state feature vector; according to the invention, based on the surface state feature vector, the defect mode category is discriminated, and the corresponding shearing correction parameter is generated.

The embodiment of the invention provides an edge detection method and device based on gradient weighted fusion and an adaptive threshold, and is applied to the field of computer vision and digital image processing. The method comprises the steps of firstly preprocessing an input image, then extracting two groups of gradient magnitude diagrams and directional diagrams through an adaptive morphological operator and a traditional difference operator, and constructing a weighted fusion function according to the gradient direction consistency of each pixel point to obtain a fused gradient magnitude diagram; and adaptively determining a high threshold and a low threshold based on an information entropy maximization principle, executing an improved Canny process by combining the fused gradient magnitude diagram and the second gradient directional diagram to obtain an initial edge diagram, and outputting a final edge detection result after dynamic structure element optimization. In this way, the defects that in a traditional edge detection method, gradient information extraction is not precise, threshold selection lacks adaptability, and an edge result is fractured can be overcome, more robust and more accurate image edge detection is achieved, and the reliability of an edge detection algorithm in a complex image scene is improved.

The invention belongs to the technical field of image processing, and particularly relates to a screw fastening quality evaluation method and system based on image features, and the method comprises the steps: collecting a screw fastening image, and obtaining a complex response diagram through a Log-Gabor filter; obtaining a phase congruency diagram according to each complex response diagram; acquiring gradient directions of pixel points in the phase consistency graph, and constructing an accumulator graph according to the gradient directions and offset points in a preset radius range; according to salient points in the accumulator graph and local signal-to-noise ratios of the salient points, screw positioning points are determined through two-dimensional quadratic polynomial function fitting; the torque value of the screw is obtained according to the screw positioning point driving torque measuring device, and whether the screw is fastened or not is judged. According to the method, the screw is positioned by using phase consistency in the frequency domain, so that the interference of specular reflection and extreme shadow on positioning is overcome, the accuracy of screw positioning is improved, and screw fastening quality evaluation is assisted.

The invention relates to the technical field of image processing, in particular to an automatic identification method for a hidden crack defect of a module, and the method comprises the steps: obtaining a gradient value and a gradient direction of each pixel point in a cell EL image, calculating an average gradient value of each column of pixel points to obtain an average curve, and taking a column where a peak point of the average curve is located as a grid line column; positioning an abnormal grid line point according to the gradient direction of each pixel point on the grid line column, correcting the gradient value of the abnormal grid line point to obtain a gradient value sequence of the grid line column, and subtracting a gradient mean value from each gradient value in the gradient value sequence to obtain local gradient distribution of the grid line column; constructing global gradient distribution according to the local gradient distribution of each grid line column; and performing threshold segmentation on the difference image of the real-time gradient distribution and the global gradient distribution of the EL image to obtain the hidden crack defect. Through the technical scheme of the invention, the hidden crack defect on the photovoltaic module can be accurately identified.

The invention belongs to the technical field of image processing, and particularly relates to a medicine production line remote monitoring method based on image processing, which comprises the following steps of: calculating a reflection intensity score according to a relative deviation and a gradient magnitude between a gray value of a pixel point in a bubble cap region and the overall gray of the region, and further determining a gray validity weight; initializing tablet center estimation, calculating an edge quality score in combination with a gray validity weight and an included angle between a gradient direction and a radial direction, and optimizing a tablet center position through an iteration gravity center; dividing a fan-shaped region by taking the optimized center as an original point, screening a reliable region and a region to be complemented according to the final edge quality score, and determining a complementation value by using the quality score interpolation of the reliable regions on the two sides; and synthesizing the gradient magnitude, the final edge score and the complementation value to construct an edge strength graph, and carrying out tablet defect detection according to a Hough circle detection result of the edge strength graph. The interference of light reflection and tablet deviation is eliminated, and the detection accuracy is improved.

The invention relates to the technical field of deep learning model training, solves the technical problem that gradient pointing is inaccurate when model parameters are updated in a traditional SAM algorithm, and particularly relates to an image classification method based on sharpness perception minimization. A gradient direction correction mechanism is introduced for an image classification task, so that the stability in the optimization process and the generalization ability of the model on image recognition test data are remarkably improved. According to the method, the gradient disturbed by the SAM algorithm is corrected by using the feature vector, and the abnormal component of the gradient in the feature vector direction is effectively reduced, so that model parameter updating is prevented from pointing to a sharp region of a loss function. The correction of the optimized path significantly improves the accuracy of gradient updating in the training process of the image classification model, so that the model can learn more discriminative visual feature representation, and finally the classification accuracy and the generalization performance of the model on a test set are improved.

The invention relates to the technical field of image processing, in particular to a real-time monitoring method for a stamping line for pipeline processing, which comprises the following steps of: acquiring a surface image of a pipeline, calculating gradient amplitudes of pixel points and a local structure main direction, generating a candidate path, and calculating the consistency of the gradient amplitudes of all the pixel points on the path and the main direction. Determining the continuity of the path structure; gradient directions of symmetrical sampling points on two sides of a normal of a main direction of a local structure of a pixel point are analyzed, gradient direction consistency is determined, fusion saliency of the pixel point is calculated by combining path structure continuity and gradient direction consistency, a fusion saliency map is generated, double-threshold segmentation processing is carried out to extract all edge contours of a pipeline, and a fusion saliency map is obtained. And determining whether the pipeline has defects or not according to the morphological characteristics of the edge contour so as to realize real-time monitoring of the quality of the pipeline in the stamping production line. The method improves the accuracy of pipeline processing monitoring.

The invention belongs to the technical field of image processing, and particularly relates to a PCB processing positioning method based on image processing, which comprises the following steps: extracting edge pixel points and a gradient direction in a gray level image of a PCB, constructing a sampling area along the gradient direction and a reverse direction, calculating a radial structure contrast ratio, and calculating a radial structure contrast ratio; taking the intersection point of the edge points and the gradient extension lines as a target for voting, forming a gradient convergence field in combination with gradient included angle weight and radial structure contrast, screening peak points as center candidate points, and calculating candidate scores in combination with gradient convergence intensity, distance standard deviation from edge pixel points to the center candidate points and a radial structure contrast mean value; and determining a reference point center according to the candidate score. According to the invention, accurate positioning of the reference point is realized, and the positioning accuracy and reliability of automatic processing of the PCB are improved.

The invention relates to the technical field of wiring robot system fault detection, in particular to a transformer wiring robot system fault intelligent detection method. The method comprises the following steps: acquiring an image in a wiring process and graying the image to obtain a grayscale image; obtaining candidate pixel points in the grayscale image and a pixel point sequence of the candidate pixel points; acquiring a local gradient amplitude change feature value and a local gradient direction change feature value of each neighbor pixel point, and further acquiring initial edge feature credibility of the candidate pixel points; obtaining the final edge feature credibility based on the initial edge feature credibility of each marked pixel point of one candidate pixel point; performing clustering analysis based on the final edge feature credibility of each candidate pixel point in a gray level image to obtain a low threshold value and a high threshold value of the gray level image; and carrying out edge detection on the grayscale image by using the low threshold and the high threshold, and identifying the abnormity of the wiring robot. According to the invention, the wiring abnormity of the wiring robot can be effectively processed.

The invention relates to the technical field of medical instrument detection, discloses a medical whole basket instrument multi-parameter detection system and method, and aims to solve the problem that in an existing CCD image detection technology, a high-brightness reflection area interferes with a detection result due to tiny bloodstain or superposition on the surface of an instrument. The system comprises an imaging module, a light source control module, a data processing module and a carrying platform. According to the method, reflection is reduced through combination of multispectral imaging and an adjustable light source, a reflection area is accurately identified and removed by using an algorithm of combining gradient amplitude and gradient direction entropy, and finally geometric parameters and cleanliness indexes of the instrument are synchronously calculated.

The invention relates to the technical field of image processing, and discloses a mechanical part contour extraction method and system based on edge detection. The method comprises the following steps: carrying out overlapping region division on a part image, implementing adaptive illumination compensation according to gray level statistics, carrying out gradient joint detection on a preprocessed image through adaptive double thresholds, screening reliable edge points according to neighborhood gradient direction deviation, continuously carrying out contour tracking connection according to a gradient direction, and carrying out contour tracking connection according to the gradient direction; and realizing inner and outer contour layered identification according to curvature statistical characteristics and topological nesting depth. The integrity of contour extraction of the mechanical part and the accuracy of layered recognition are improved.

The invention discloses a ship bollard identification method and system based on adaptive multi-scale multi-grid division, and belongs to the technical field of computer vision and image processing. The system divides an image foreground region and a background region through a visual saliency calculation module, carries out dense sampling in the foreground region and sparse sampling in the background region by adopting a non-uniform grid generation module, screens candidate grids in combination with gradient direction consistency and texture features, fuses candidate frames of different scales through a multi-scale image pyramid, and finally obtains a multi-scale image. And accurate positioning of the bollard is realized through the fine grid accurate positioning module. The method solves the problem that the prior art is insufficient in adaptability to ship size, shooting distance and resolution change, improves the precision and generalization ability of bollard positioning in a complex scene, and is suitable for bollard detection scenes of various ship images.

The invention relates to the technical field of product design, in particular to a household appliance appearance defect optimization product design method based on image discrimination, which comprises the following steps: acquiring a household appliance appearance image, calculating a gradient direction, constructing a distribution matrix, analyzing consistency to generate a defect edge image, extracting high-frequency feature scores to identify defect areas, and clustering point cloud to output defect clusters. According to the method, by calculating the gradient direction numerical value of each pixel point and constructing the gradient direction distribution matrix, the processing capacity of the directivity difference of the household appliance surface texture is effectively improved, the defect point cloud is accurately divided through the density-based noise application spatial clustering algorithm, the defect point cloud is accurately divided, and the defect point cloud is accurately divided. According to the method, the space positioning accuracy of the defect area is improved, the precision of optimization design is ensured, curved surface reconstruction is performed in combination with the least square method, the appearance design of the household appliance is accurately corrected, and the production efficiency and the design quality are optimized.

The invention relates to the technical field of image processing, in particular to a lens edge detection method and system based on image generation, and the method comprises the following steps: collecting a lens image through an industrial camera, carrying out the weighted graying, extracting an edge coordinate through a Laplace operator, constructing an edge pixel coordinate set, and carrying out the image processing; screening an edge validity marking result by combining a gray difference threshold, calculating a phase difference between a gradient direction and light source incidence based on a Sobel operator, extracting pixels in a consistent direction, carrying out spatial clustering, generating a lens edge pixel cluster, and fitting a continuous curve to construct a complete lens edge contour. Noise and reflection interference are restrained through pixel neighborhood gray difference, effective pixels are screened in combination with a gradient direction and light source incident phase relation, edge direction consistency is enhanced, pixels are clustered according to spatial continuity and gradient intensity, contours are continuously fitted based on pixel cluster distribution, and lens edge integrity and geometric consistency under complex illumination are improved.

The invention provides an unmanned vehicle local high-precision positioning mapping system and method based on reinforcement learning polarization normal deambiguity and depth information fusion, and the method comprises the steps: synchronously obtaining a polarization image and a depth image of a target scene in the driving process of an unmanned vehicle in a low-texture and low-illumination scene; constructing the polarization normal candidate, the depth normal, the neighborhood surface curvature and the depth gradient information into a state vector, inputting the state vector into a reinforcement learning agent based on Dueling DQN, and obtaining the depth gradient according to a reward function fusing the included angle error of the polarization normal and the depth normal, the neighborhood normal smoothness loss and the consistency error of the polarization normal and the depth gradient direction. Outputting an optimal polarization normal selection action to obtain an unambiguous surface normal; polarization estimation depths are generated, and confidence coefficients are calculated respectively; according to the depth confidence coefficient and the polarization confidence coefficient, pixel-level weighted fusion is carried out, a fused depth map is obtained, synchronous positioning and mapping are carried out on the fused depth map and the synchronous RGB image, and a point cloud map of the target scene and the moving track of the unmanned vehicle are output.