1437 results about "Textural feature" patented technology

The invention provides an engineering construction defect automatic detection and classification method based on deep learning, and the method comprises the steps: obtaining a welding seam surface image through the shooting of an unmanned plane, and carrying out the denoising and illumination normalization processing of the welding seam surface image, and obtaining a standardized image; welding seam surface texture features are extracted from the standardized image, a convolutional neural network is adopted to analyze the spatial distribution characteristics of textures, and vectorization processing is carried out to obtain texture feature vectors; segmenting a weld surface corresponding to abnormal region distribution by adopting a region growing algorithm, and analyzing pore and weld discontinuity in combination with the texture feature vector to obtain a defect candidate region; performing threshold division on the sizes and the numbers of the defects according to the defect types and the feature vectors of the candidate regions to obtain a severity grading result of each type of defects; and severity features are extracted from a grading result, and a Bayesian network is adopted to fuse texture feature vectors and defect type labels to obtain a welding quality evaluation score.

The invention relates to a fabric flaw detection and traceability system based on edge calculation and computing power scheduling, which is suitable for intelligent quality control in a textile production process. The system comprises an acquisition unit, a modeling unit and the like. The acquisition unit acquires fabric images and environmental data through a multispectral imaging device and a process parameter sensor, and constructs time-aligned multi-modal feature tensors. The modeling unit extracts texture features by using unsupervised comparative learning in combination with fabric material characteristics, and generates potential texture fingerprint vectors. And the detection unit adopts a target detection network of a channel attention mechanism to identify fabric flaws and output positions, types and severity. The traceability unit analyzes correlation between defects and process parameters through time sequence causal reasoning, and constructs a causal atlas. And the optimization unit generates a process optimization vector according to the causal atlas and the risk score, and realizes visual display and edge control feedback, thereby constructing a real-time defect control and explainable traceability-oriented closed-loop quality management system.

The invention relates to the technical field of cultural heritage digital protection, in particular to a super-volume historic building three-dimensional simulation modeling method based on multi-source heterogeneous data, and the method comprises the steps: firstly collecting node multi-source heterogeneous data such as laser point cloud, images, structural mechanical parameters and historical repair records, and then carrying out node feature enhancement through a node feature enhancement module; using an improved generative adversarial network to strengthen node edge features, adopting an adaptive threshold segmentation algorithm to extract surface texture features, converting mechanics and size data into a three-dimensional constraint condition parameter matrix, then using a topological relation verification algorithm, using a graph neural network to traverse and verify a component connection relation, and obtaining a three-dimensional confrontation model; and a re-calibration mechanism is triggered when the deviation exceeds the limit, the weight is adjusted based on a Bayesian optimization algorithm, fusion verification is carried out again, finally, hierarchical grid division is adopted to construct high-precision sub-models, and the sub-models are spliced into an integral three-dimensional model, so that the model precision and reliability are improved, and reliable digital support is provided for ancient building protection.

The invention discloses a small sample industrial defect detection system based on a multi-stage diffusion model. The small sample industrial defect detection system comprises a multi-stage diffusion generation module and a defect detection module based on multi-scale attention and physical constraint. The multi-stage diffusion generation module divides the diffusion generation process into three stages of global structure reconstruction, local detail refinement and texture feature synthesis through a stage control mechanism, and gradually guides feature evolution and improves the generation effect aiming at the quality and diversity problems of defect image generation under the small sample condition; the defect detection module based on multi-scale attention and physical constraint adopts a coding structure fusing local window attention and global attention, through multi-scale feature extraction and fusion, significant features of a defect area are effectively captured, gradient smoothing loss and edge energy consistency loss are introduced at a decoder end, and the defect detection accuracy is improved. Therefore, high-quality reconstruction of a normal area and effective suppression of an abnormal area are realized.

The invention relates to a medical image segmentation method and system based on residual Mama and multi-scale boundary enhancement. The method comprises the following steps: acquiring and preprocessing a medical image; inputting the image into a segmentation model based on an encoder-decoder architecture; the encoder synchronously extracts local texture features and models long-range spatial dependence through residual error convolution blocks and residual error Mama blocks which are alternately connected; fusing and enhancing the jump connection features between the encoder and the decoder through a boundary enhancement module to optimize boundary characterization; integrating a multi-scale gating attention module in a decoding path, and adaptively selecting and fusing multi-scale context features; and finally outputting the high-precision segmentation mask. The method effectively solves the problems that in the prior art, long-range dependence and local details are difficult to consider, the multi-scale feature fusion capability is insufficient, boundary segmentation is fuzzy and the like, and the segmentation accuracy, the boundary continuity and the clinical practicability are remarkably improved.

The invention provides an aluminum film sealing defect real-time detection method and system based on multi-algorithm fusion, and relates to the technical field of intelligent manufacturing, and the method comprises the steps: triggering an industrial camera at a detection station to collect an original image of a pesticide aluminum film sealing on a conveyor belt; performing adaptive equalization operation on the original image through pixel brightness distribution data, eliminating light fluctuation and surface reflection interference, and outputting a standardized image; three types of defect detection are synchronously executed based on the standardized image, a dynamic threshold segmentation algorithm is combined with local area brightness analysis to detect edge damage, a contour extraction algorithm is adopted to calculate bottleneck center offset to recognize seal offset, wrinkle defects are recognized based on a surface texture feature analysis algorithm, and a primary detection result is output. The aluminum film sealing defect detection method is based on multi-algorithm fusion, has strong anti-interference capability, real-time detection performance and data traceability, and provides an efficient and reliable automatic solution for aluminum film sealing quality management and control.

The invention discloses a space-time spectrum combined super-resolution reconstruction method based on a giant remote sensing satellite group. The method comprises the following steps: acquiring time series, multi-view and multi-spectral data of the same area from a plurality of heterogeneous satellites, and performing radiometric calibration and atmospheric correction; sub-pixel-level alignment of the multi-source data is realized by adopting a joint registration model; extracting time change features by using three-dimensional convolution, extracting space structure and texture features by using two-dimensional convolution, and extracting and reducing the dimension of spectral features by using one-dimensional convolution; performing adaptive weighted fusion on time, space and spectral features through an attention mechanism to generate a joint feature tensor; and carrying out super-resolution reconstruction to obtain a target image with high spatial resolution, high time resolution and high spectral fidelity. The method gives consideration to both resolution improvement and spectrum authenticity, and is suitable for high-precision remote sensing application scenes such as fine urban mapping, agricultural monitoring, ecological environment assessment, disaster emergency and battlefield situation awareness, remote reconnaissance, target change detection and damage assessment.

The invention belongs to the technical field of industrial detection, and discloses a defect detection method and system based on honeycomb catalyst stacking. Omnibearing image data of honeycomb catalyst stacking are obtained through a multi-angle polarization imaging technology, pixel-level polarization degree parameters are calculated to construct a global polarization feature map, and accurate distinguishing between an intrinsic porous structure and suspected defects is achieved. A blind area identification and virtual view angle reconstruction mechanism is introduced, so that the problem of a stacked edge detection blind area is solved; and a layered reflectivity compensation function is adopted, so that the optical interference of an interlayer overlapping region is eliminated. Texture features are extracted through multi-scale morphological filtering, multi-dimensional feature fusion is carried out in combination with polarization features, edge continuity indexes and correction reflection intensity, and a high-precision defect discrimination model is established. And for a low-confidence region, dynamically adjusting detection parameters and performing iterative optimization to form an adaptive detection closed loop. According to the invention, the detection precision and reliability are improved, and the defect position, type and severity can be accurately output.

The invention provides a computer visual defect detection system and method on an electric control board production line, and relates to the technical field of data processing.The method comprises the steps that according to collected multi-angle images, the overall area of an electric control board is partitioned in combination with illumination conditions and visual angle information; performing image partitioning processing to generate a plurality of image sub-regions, and extracting texture features, brightness distribution features and geometric edge features in each image sub-region; rare defect feature enhancement processing is executed, multi-scale repeated superposition is carried out on low-frequency abnormal textures, and directional extension is carried out on edge fractures; performing difference comparison with the corresponding normal area combination features, and performing normalization correction in combination with the illumination condition and the visual angle information; multi-angle reproducibility analysis is executed, when the same suspected defect is detected at different angles, a reliable defect area is formed, otherwise, an interference area is removed, and an electric control board defect detection result is generated; according to the invention, the accuracy of defect detection is improved.

The invention discloses a metal ore body identification method based on remote sensing interpretation, relates to the technical field of mineral resource exploration, and aims to solve the problems of low identification precision, neglect of geological laws and poor generalization caused by dependence on single remote sensing data in the prior art. After standardization processing, spectrum and texture features are fused to generate a composite vector; extracting a multi-scale mineralization signal by using a convolutional neural network, and interpreting and constructing an ore control area in combination with a terrain gradient and a curvature; alteration zoning is identified through spectrum angle matching, and a favorable mineralization area is determined by combining lithology combination analysis; and dynamically weighting geological elements by adopting a deep learning fusion network, outputting an ore body position according to a comprehensive score, and finally optimizing a boundary through spatial clustering and morphological filtering. The method significantly improves the automation degree and reliability of ore body identification in a complex environment, and is suitable for metal mineral exploration target area delineation.

The invention discloses an oral tooth lesion AI auxiliary diagnosis system, which belongs to the field of artificial intelligence and comprises an image acquisition module, a three-dimensional modeling module, a lesion marking module, a dual-channel feature extraction module, a cross-modal diagnosis reasoning module, a lesion evolution trend prediction module and a dynamic risk level generation module. The image acquisition module utilizes multi-frequency structured light and a polarization camera to cooperatively acquire oral images; the three-dimensional modeling module is used for reconstructing an upper and lower jaw three-dimensional structure based on edge constraint splicing point clouds and registering images to form double-view fusion data; the lesion labeling module fuses expert labeling and weak supervision pseudo labels to generate joint labels; the dual-channel module extracts skeleton and texture features; the reasoning module realizes cross-modal semantic coupling through an image-text co-occurrence graph; the evolution prediction module models a lesion change path based on the time reversal causal network; and the risk module outputs a five-level risk and re-injects the embedded vector to strengthen prediction. The beneficial effects are that diagnosis intelligence and clinical decision support level are obviously improved.

The invention relates to the technical field of industrial automation and process control, in particular to a digital visual control system and method for automation equipment. The method comprises the following steps: carrying out image sequence acquisition and preprocessing on the material conveying automation equipment to obtain an enhanced flow image set; performing flow texture and flow velocity distribution analysis on the enhanced flow image set to obtain a texture feature data set and a velocity field distribution diagram; performing flow state feature extraction on the texture feature data set and the velocity field distribution map to obtain a flow state feature vector map; performing three-dimensional point cloud construction on the material to obtain a stacking curved surface model; and carrying out repose angle measurement and partition processing on the accumulation curved surface model to obtain a repose angle distribution diagram. By means of the industrial automation and process control technology, control normal form transformation from passive response to active prevention is achieved, and the stability and accuracy of the powder material conveying and subpackaging process are remarkably improved.

The invention provides an intelligent pipe surface quality detection method and system based on machine vision, relates to the technical field of industrial automatic detection and machine vision, and aims to establish a pipe surface feature library and mark shape abnormal features. The method comprises the following steps: collecting an image of a pipe in a bright and dark composite light field, extracting gray and texture features after polarization filtering processing, reconstructing a three-dimensional point cloud covering a mortar layer and a concrete layer by matching a principal component analysis dimensionality reduction fusion feature set with a feature library, and converting the point cloud into a two-dimensional expansion graph through cylindrical projection; a mortar abnormal area and a concrete abnormal area are segmented, the hole volume is calculated through point cloud residual errors in the mortar area, and internal hollowing is detected in combination with acoustic vibration excitation and thermal response; the concrete area locates defects based on point cloud features; according to the sequence of the concrete covering process before the mortar covering process, a correlation model is constructed to match and coincide the defect sites, and the detection result is output, so that the detection automation degree and accuracy can be improved.

The invention discloses a flexible stone texture defect identification method based on a multi-scale convolutional neural network, and the method comprises the following steps: collecting images of the surface of a flexible stone, and carrying out the batch classification; selecting a first image of each production batch as a batch first sample, and generating batch configuration parameters; performing texture feature extraction by using the batch configuration parameters and the to-be-detected image to generate a texture map; respectively inputting the to-be-detected image into a spatial domain convolution branch and a frequency domain convolution branch of the space-frequency neural network model, and extracting spatial domain features and frequency domain features according to the scale control information; the spatial domain features and the frequency domain features are fused; and generating candidate areas based on the fused features, performing positioning and confidence evaluation, removing the candidate areas with confidence smaller than a preset threshold, and generating a flexible stone texture defect detection result. According to the method, the surface defects of the flexible stone can be accurately detected, the detection efficiency and robustness are improved, and the manual detection cost is reduced.

The invention relates to the technical field of industrial nondestructive testing, in particular to a paper drum defect full-inspection method based on machine vision, which comprises the following steps: step 1, constructing a curved surface self-adaptive optical environment: arranging annular low-angle strip-shaped light source arrays along the circumferential direction of a paper drum at equal intervals, and forming a dynamically adjusted set acute angle between the emergent direction of each light source and the surface normal of the paper drum, the acute angle is reduced along with the increase of the diameter of the paper barrel, and near-infrared coaxial diffused light sources are arranged at the two axial ends of the paper barrel; step 2, motion compensation three-dimensional reconstruction; 3, multi-modal defect hierarchical decision making: expanding the three-dimensional point cloud into a two-dimensional image along the circumferential direction, and constructing a dynamic reference grid with the grid cell size adaptively adjusted along with the pattern complexity on the two-dimensional image; cooperatively extracting geometric structure features, surface texture features and gluing seam features; and outputting defect classification through a three-level decision tree. By means of the method, efficient and accurate paper barrel full inspection can be achieved, and the automation level of a production line and the paper barrel quality control capacity are effectively improved.

The invention relates to the technical field of road engineering safety monitoring, in particular to an expressway slope crack identification method based on unmanned aerial vehicle laser point cloud visualization, which comprises the following steps: multi-source data collaborative acquisition: an unmanned aerial vehicle carrying a laser radar scanner and a high-resolution optical camera flies along a multi-angle combined route, and the unmanned aerial vehicle carries a laser radar scanner and a high-resolution optical camera; synchronously acquiring side slope three-dimensional laser point cloud data and an orthoimage sequence; data fusion preprocessing: denoising and filtering the data to generate an exposed slope triangular mesh curved surface model, and mapping textures to generate a high-precision live-action three-dimensional model; performing multi-dimensional feature fusion recognition, extracting curvature and texture features, inputting the curvature and texture features into a pre-training classification model, judging and outputting a crack pixel-level position; and carrying out visual output, superposing crack information rendering and generating a quantitative report containing the length and width of the spatial position. The method is high in coverage precision and identification accuracy, and provides a reliable basis for slope safety assessment.

The invention discloses an aircraft surface crack intelligent detection and measurement method combining an image and a point cloud. The method comprises the following steps: carrying out two-dimensional image and three-dimensional point cloud data acquisition on an aircraft surface; performing crack defect identification on the acquired two-dimensional image based on a target detection model; aligning the two-dimensional image with the three-dimensional point cloud, and acquiring corresponding crack defect three-dimensional point cloud data according to the crack defect position identified from the two-dimensional image; and performing filtering and down-sampling processing on the obtained three-dimensional point cloud data of the crack defects, and performing crack defect size measurement on the processed three-dimensional point cloud data of the crack defects to obtain length, width and depth information of the crack defects. An omnibearing defect detection system is constructed by fusing two-dimensional image processing and three-dimensional point cloud analysis technologies; the texture features of the two-dimensional image and the depth information of the three-dimensional point cloud are organically combined, so that the detection capability of the system is improved, and accurate identification and size measurement of the aircraft surface crack defects are realized.

The invention relates to the technical field of medical image processing, and particularly discloses a medical image segmentation method with adaptive receptive field and feature correction, which comprises the following steps: (1) acquiring an original medical image and a segmentation label thereof, and constructing a training and testing data set; (2) carrying out size normalization and enhancement processing on the image; (3) establishing an improved U-shaped encoder-decoder segmentation network, introducing an adaptive branch mixed shape convolution module in a shallow layer, and improving edge and texture feature modeling capability by adopting a multi-branch banded convolution and channel attention mechanism; (4) a residual directional feature interaction module is introduced into a deep layer, a spatial dependency relationship is modeled through an information interaction structure in the horizontal and vertical directions, and the direction sensing ability of the heterostructure is enhanced; and (5) completing network training and reasoning, and outputting a segmentation result. The method gives consideration to the calculation efficiency and the segmentation precision, and is suitable for the automatic segmentation task of various types of medical images with complex structures.

The invention discloses an asphalt pavement disease identification method based on unmanned aerial vehicle scanning, and relates to the field of asphalt pavement disease identification, and the method comprises the steps: employing an unmanned aerial vehicle to carry a multispectral imager, obtaining an orthoimage of a whole road segment through a preset route, and carrying out the preliminary feature extraction and suspected region identification; a flight path is optimized according to a suspicion degree and spatial distribution by intelligently planning a recheck route for a suspicion area, and spectral reflectivity data of a specific wave band is collected for secondary verification; color features, glossiness features and texture features are explicitly extracted for a target asphalt pavement area, quantitative extraction is carried out on disease related physical features through brightness and saturation distribution, highlight area detection, texture structure analysis and other means, the accuracy and reliability of oil flooding disease recognition are improved, meanwhile, the disease degree grade is generated, and the recognition efficiency is improved. And accurate data support is provided for asphalt pavement maintenance decision making.

The invention discloses a photoelectronic device surface defect visual detection method and system based on image recognition, and the method comprises the steps: carrying out the processing and fusion of three-waveband image data, and obtaining a three-waveband composite image; constructing a DFD-Net double-branch deep network model, inputting a three-band synthetic image into the model, capturing multi-scale defect geometric features through an ASPP module with increasing voidage, extracting reflective insensitive texture features through a phase-consistent convolutional layer, and performing feature fusion by using a gated cross attention mechanism to obtain a multi-scale defect feature fusion model; performing coarse positioning on the fused feature image based on an improved YOLOv5 target detection algorithm to obtain a defect area image, and calculating a pixel-level mask of the defect area image to obtain a segmented image; and positioning a defect boundary of the segmented image through an NMS non-maximum suppression algorithm, and outputting defect coordinates and size information. And the surface defect detection efficiency of the optoelectronic device is improved.

The invention provides a digital evaluation method and system for fair-faced concrete based on Mockup, and belongs to the field of building construction. According to the technical scheme, the method comprises the steps of collecting a surface image of a bare concrete Mock sample plate, extracting a reference Lab color value and a reference texture feature from the collected sample plate image, and constructing an evaluation reference model; collecting a surface image of the to-be-evaluated bare concrete member, and generating image data; performing defect area identification on the image data, extracting an effective image area, and extracting a Lab color value to be evaluated and a texture feature to be evaluated from the effective image area; generating a comparison result based on the evaluation reference model; and based on the comparison result, generating evaluation output information. The method has the beneficial effects that the color and texture double-feature model is constructed, a defect identification and shielding mechanism is introduced, and a structured comparison algorithm and an output system are adopted, so that the automation of the whole process from data acquisition, feature extraction, defect avoidance to intelligent comparison and evaluation output is realized.

The invention discloses an intelligent segmentation method and device for ceramic matrix composite CT image defects, and belongs to the technical field of image detection. The method comprises the following steps: inputting preprocessed CT image data into a preset double-branch collaborative segmentation model, and respectively outputting to obtain a local texture feature map and a global relation feature map; performing three-branch material perception fusion processing on the local texture feature map and the global relation feature map according to a preset perception fusion model to obtain a fusion feature map containing local detail accuracy and global consistency; sequentially carrying out progressive decoding and refined reconstruction processing on the fused feature graph to obtain a defect probability graph; and calculating a loss function of the defect probability graph and a manually pre-labeled real defect segmentation graph, and dynamically adjusting the loss weight of the initial model according to a calculation result to obtain a CT image defect segmentation model meeting a preset convergence condition. The method can meet the detection requirements of industrial scenes on the defects of the ceramic-based composite material.

The invention relates to the field of defect detection, in particular to a wallpaper defect detection method and system based on machine vision. The method comprises the following steps: analyzing acquired to-be-detected wallpaper image data to obtain regional texture variance, edge density and brightness gradient indexes, and generating a scale image layer set; extracting salient edge points in each scale image and generating an edge anchor point set; analyzing the scale image layer set and the edge anchor point set to obtain a texture difference index, and generating a texture feature map set according to the edge anchor point set; processing the texture feature map set to generate a texture significance distribution map; based on the texture significance distribution diagram, main direction distribution is extracted, a direction residual error model is constructed, and a residual error diagram and a direction difference distribution diagram are generated; and establishing a region scoring matrix, and generating a wallpaper defect credibility distribution map and a defect classification image output set on the basis of the region scoring matrix. The wallpaper defect detection precision can be improved.

The invention relates to the technical field of roadway surrounding rock danger identification, and discloses a roadway surrounding rock danger identification model construction method, which comprises the steps of collecting multi-modal data, and generating preprocessed data through synchronous calibration and denoising; extracting a seismic wave frequency domain and image texture features, and generating a multi-modal feature matrix; in combination with a geological prior clustering mining abnormal mode, generating a labeled sample data set; generating a danger identification model based on a transfer learning and feature fusion training network; and the edge deployment model performs real-time reasoning, and generates an early warning result through an adaptive algorithm. According to the method, the frequency domain features of the seismic fluctuation signals and the depth texture features of the surrounding rock images are fused, the multi-modal feature matrix is constructed, abnormal mode mining is carried out in combination with geological prior knowledge, and early weak abnormal signals such as hidden fault slippage or asymmetric microfracture extension which are difficult to find by a single monitoring means can be effectively recognized.

The invention relates to a metal surface defect identification method and device based on artificial intelligence, equipment and a medium. The identification method comprises the following steps: synchronously acquiring an electromagnetic impedance signal and an optical image of a metal surface based on an eddy current sensor and an industrial camera; generating a spatially positioned interference area mask by quantifying cross-physics field interference intensity of the electromagnetic gradient and the optical edge feature; performing skin effect compensation on an electromagnetic signal by using the mask, and simultaneously performing shadow suppression on an optical image to realize physical field decoupling; a double-branch neural network is called to fuse the decoupled electromagnetic impedance features and optical texture features, and cross-modal associated joint feature expression is established; and analyzing the defect probability heat map based on the fused features, and outputting defect types and coordinates in combination with geometric feature matching. According to the method, the problem of defect feature mixing caused by mutual interference of electromagnetic-optical signals is solved, and the recognition precision and the positioning reliability of the micro defects are remarkably improved.

The invention relates to the cross technical field of computer vision and civil engineering, and discloses a deep learning-based bridge disease image segmentation method, which comprises the following steps of: establishing an image data set containing crack and spalling diseases and performing online enhancement; constructing a segmentation network model comprising a frequency dynamic convolution encoder branch, an edge enhancement Transform encoder branch, a gating cooperation unit, a decoder and a depth supervision module; training the model by using a weighted mixed loss function; and inputting the test set to obtain a final segmentation mask. Self-adaptive fusion of local texture features and global context information is realized through a dual-encoder architecture and a gating cooperation mechanism; a frequency dynamic convolution and edge enhancement module is utilized to enhance the anti-noise capability and micro-disease perception under a complex background; and in combination with a category weighting strategy, the problem of pixel category imbalance is effectively solved, and high-precision automatic segmentation of concrete bridge diseases is realized.

The invention relates to the technical field of medical image analysis, computer vision and deep learning, in particular to a texture perception and multi-scale feature adaptive fusion hip joint osteophyte detection method. According to the method, firstly, a bone texture feature extraction module is used for carrying out feature extraction and enhancement on a hip joint X-ray image, a parallel double-branch structure is adopted, gradient and space structure features are extracted through a Sobel operator and pooling operation, and feature maps are fused; then, the fused features are input into a double-backbone network, the first backbone network extracts local fine textures and long-range dependence features by combining convolution, self-attention and a gating mechanism, and the second backbone network extracts multi-scale semantic features through hierarchical grouping and depth separable convolution; and double-trunk output is dynamically weighted and fused through an adaptive fusion module, features are optimized through a multi-scale semantic fusion module, and finally the position and confidence of osteophyte are output.

The invention relates to the field of computer vision, in particular to an image feature recognition method based on computer vision, which comprises the following steps of: preprocessing an input image, removing image noise and correcting image gray deviation to obtain a preprocessed image; performing multi-scale feature extraction on the preprocessed image, obtaining image local texture features and global contour features under different scales, performing dynamic weight fusion, calculating dynamic fusion weight based on a feature response value and a local complexity factor, and performing dimension reduction processing on fusion features through a principal component analysis algorithm to obtain a target feature set; and matching the reference features, calculating the Euclidean distance between the target feature set and the reference features, determining an adaptive matching threshold, judging a matching result, and completing image feature recognition. According to the method, through adaptive filtering and gray level equalization preprocessing, Gaussian pyramid multi-scale feature extraction and dynamic fusion and adaptive threshold matching, feature extraction accuracy, characterization capability and recognition robustness are improved.

The invention discloses a sealing ring defect detection method and system based on computer vision, and relates to the technical field of sealing ring defect detection.The method comprises the steps that an edge contour and surface texture features are extracted from an original sealing ring image through a preset convolutional neural network, and a geometric characteristic distribution diagram is obtained; calculating the defect occurrence weight of each region, and determining boundary coordinates of the key region; acquiring an enhanced image sequence for the key area; a sharp key area sub-image is obtained; detecting positions and types of fine defects by using a preset residual network aiming at the standard sub-images to obtain defect classification labels; generating a defect distribution thermodynamic diagram according to matching of defect classification labels and boundary coordinates of the key region; superposing the original image through the defect distribution thermodynamic diagram, and outputting an overall defect evaluation report; through integration of feature extraction, region positioning, imaging optimization and defect detection, the precision and efficiency of sealing ring defect detection are remarkably improved, and an intelligent solution is provided for industrial quality inspection.

The invention relates to the technical field of tunnel crack detection and artificial intelligence edge calculation, and provides a tunnel apparent disease detection method based on deep learning and knowledge distillation, which comprises the following steps: step 1, introducing spectral domain information enhancement to an original tunnel image, the edge texture features of the disease area in the image are enhanced through methods such as multi-scale wavelet transform and small-scale enhancement. Step 2, constructing a high-performance teacher model, introducing a flexible up-sampling structure to adapt to feature recovery requirements of different levels of semantic information, introducing an efficient visual coding module to enhance feature fusion capability of different scale channels, and designing a scale adaptive weighted loss function at the same time; by introducing a frequency spectrum enhancement mechanism, structural features of disease areas with low contrast, fuzzy edges and the like are remarkably enhanced in an image preprocessing stage, clearer information input is provided for a model, and the stable recognition capability of a system in environments of uneven illumination, complex background and the like is enhanced.