558 results about "Surface map" patented technology

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention provides a power adapter appearance quality detection method and system based on machine vision, and particularly relates to the technical field of power adapter appearance quality detection.The method comprises the steps that a main control module controls a light source module to output an illumination condition matched with a surface material of a power adapter, and an industrial camera is triggered to collect a surface image; preprocessing the image to generate preprocessed image data; inputting the preprocessed image data into a defect identification model to extract defect features and generate a defect classification result; if the classification result contains the reflective interference mark, obtaining material information through an infrared sensor, adjusting light source parameters, and re-collecting and processing the image; otherwise, generating a quality detection signal according to a classification result, and transmitting the quality detection signal to a classification execution mechanism to separate the defective power adapter. According to the method, self-adaptive high-precision detection on a low-cost embedded hardware platform is realized, and the efficiency and the adaptability are improved.

The invention discloses a metal product defect detection method and system based on image recognition. The method comprises the following steps: firstly, executing reflection disturbance digestion processing on a surface image of a to-be-detected metal product to obtain a reflection digestion image; obtaining surface reference texture features of the defect-free metal product, and generating a reference texture model on the basis of a texture distribution rule, a gray average value and texture continuity; performing defect texture gradient separation on the reflection resolution image based on a reference texture model, positioning an abnormal region, and performing segmentation to obtain a suspected defect texture region; performing boundary pixel reconstruction on the suspected defect texture region to obtain a defect texture reconstruction region; obtaining a target defect texture region through local variance enhancement and neighborhood correlation analysis; and finally, the overexposure area is positioned, gray inverse stretching processing is performed, abnormal pixel group feature clustering is performed on the preprocessed defect area, and then a surface defect detection result is output, so that the precision and accuracy of metal product surface defect detection are improved, and the false detection rate is reduced.

The invention discloses a multi-modal dynamic compensation road disease intelligent detection and risk assessment system, and relates to the technical field of artificial intelligence and computer vision, and the system comprises an image collection module which is used for obtaining a road surface image in real time through a camera device, and transmitting the image to a preprocessing module; the preprocessing module is electrically connected with the image acquisition module and is used for carrying out graying, noise reduction, contrast enhancement and geometric correction operation on the image and outputting a standardized image; the feature extraction module is electrically connected with the preprocessing module. According to the road disease detection system provided by the invention, by integrating a plurality of modules, high efficiency and intelligence of road disease detection are realized, compared with traditional manual inspection, the system not only improves the detection efficiency, but also remarkably enhances the objectivity and accuracy of detection, and is particularly suitable for real-time monitoring requirements of a large-scale road network; the image acquisition quality is effectively improved, and the effectiveness of feature extraction can be ensured.

The invention relates to the technical field of rock mechanical tests and safety monitoring, and discloses a rock fracture prediction method based on acoustic emission and surface strain cooperative monitoring, and the method comprises the steps: building a monitoring system comprising acoustic emission and an industrial camera array, and building a unified timestamp of each subsystem through a GPS time service module; through load triggering logic, a synchronous trigger is utilized to synchronously acquire an acoustic emission signal and a sample surface image sequence, and evolution characteristics of three-dimensional coordinates of an acoustic emission source and surface full-field strain data are solved. And on the basis, calculating a main strain field variation coefficient, and performing cross-correlation analysis on the resampled energy rate and strain rate. And finally, according to multi-parameter coupling criteria of acoustic emission time sequence parameters, positioning events and surface strain, identifying rock fracture precursor types, and predicting fracture moments and areas by combining a Voight model inverse velocity method and a seismic source projection technology.

The invention discloses an elevator steel wire rope wear real-time monitoring system and equipment thereof, and relates to the technical field of safety monitoring, the elevator steel wire rope wear real-time monitoring system comprises an acquisition and extraction module which acquires real-time surface image data and vibration signal data of a steel wire rope through a sensor array, extracts surface damage features and vibration spectrum features by adopting an image processing algorithm, and sends the surface damage features and the vibration spectrum features to a server; fusing kernel function selection and hyperplane separation to process the preliminary wear feature set; the crack identification module is used for fusing tensile strength and corrosion resistance data in the material characteristic database according to the initial wear characteristic set, classifying potential fatigue crack types through support vector identification and crack morphological characteristics by adopting a support vector machine algorithm, and determining a fatigue crack distribution diagram; according to the elevator steel wire rope abrasion real-time monitoring system and equipment thereof, accurate abrasion evaluation and dynamic maintenance optimization are achieved, the safety of the steel wire rope is improved, and the service life of the steel wire rope is prolonged.

The invention discloses a building outer wall falling risk detection method and system based on deep learning, and the method comprises the steps: obtaining an original surface image, extracting vertical face geometric features, constructing a homography matrix based on vanishing points, carrying out the geometric correction, and reconstructing an orthographic projection image; performing enhancement processing on the orthographic projection image, extracting an effective detection area and intercepting a detection image; inputting the detection image into a deep learning segmentation model into which edge weight constraint is introduced, and performing semantic segmentation, binaryzation and optimization to obtain a falling region; a mapping relation is established according to the actual physical size of the building outer wall, and pixel information of the falling area is converted into multi-dimensional parameters such as the actual physical area, the elevation, the width and the height; and combining the defect intensity input quantification risk assessment model to calculate a comprehensive risk index, determining a risk level and outputting a result. Automatic identification and quantitative evaluation can be realized, distortion and background interference are eliminated, accurate conversion from pixels to a multi-dimensional physical space is realized, and a scientific basis is provided for safety investigation.

The invention provides a bearing ring surface defect detection method based on an improved YOLOv11 network. The method comprises the following steps: constructing an improved YOLOv11 network model; wherein in the backbone network and the neck network, an original standard convolution module of the YOLOv11 network architecture is replaced by a receptive wild coordinate attention convolution module; a surface detail fusion module is arranged on each of three feature map paths with different scales output from the neck network to the head network; a positioning loss function is configured to be a Focaler-DIOU loss function; training the model; and obtaining a to-be-detected bearing ring surface image, and inputting the to-be-detected bearing ring surface image into the trained model to obtain surface defect information. According to the method, the feature extraction capability of a network model on micro defects is remarkably improved, the detection performance of the network model on multi-scale and polymorphic defects is enhanced, and the positioning precision and convergence efficiency of the network model on irregular defects are improved.

The invention discloses an autoclaved aerated concrete member surface defect intelligent identification system based on image processing, and particularly relates to the field of defect identification, comprising an image acquisition module, an image preprocessing module, a defect candidate region extraction module, a defect identification and classification module, and a result output and alarm module; according to the method, a high-definition industrial camera is used for collecting a component surface image, and adaptive median filtering and a Retinex algorithm are adopted for image denoising and enhancement, so that the influence of noise and uneven illumination is eliminated; utilizing an improved multi-threshold segmentation and Canny edge detection algorithm to accurately extract a defect candidate region; the method comprises the following steps: extracting three types of feature parameters of shape, texture and gray scale, and inputting the three types of feature parameters into a deep learning model taking ResNet50 as a basic network to realize automatic identification and classification of four types of typical defects of cracks, holes, unfilled corners and surface peeling; and finally, the system divides severity levels according to the defect size, and triggers differentiated visual alarm and linkage control.

The invention discloses a steel surface defect detection method and system based on an improved YOLOv8 network. The method comprises the following steps: firstly, preprocessing an input steel surface image; inputting to an improved YOLOv8 network, wherein the network sequentially comprises a backbone network, a neck network and a decoupling detection head; carrying out multi-scale feature extraction on an input image through a backbone network, and transmitting an output feature map containing high-frequency detail information to a neck network; the neck network receives the feature map output by the backbone network, performs cross-scale feature fusion on the feature map, and generates a fusion feature map with global context information; and the decoupling detection head receives the fusion feature map output by the neck network, carries out decoupling processing on the fusion feature map, and respectively outputs defect category classification information and bounding box regression information. According to the method, the surface defects of the steel product can be accurately and efficiently detected in real time in the manufacturing process, so that the quality, integrity and use safety of the product are ensured.

The invention discloses a casting surface defect automatic detection method, which comprises the following steps of: synchronously acquiring multi-moment surface images and morphology data of a casting through multiple channels, automatically partitioning and extracting texture attributes, and realizing accurate space-time indexing; a denoising and brightness normalization algorithm is adopted to improve the consistency of basic data; in combination with a deep learning segmentation model and feature analysis, time sequence space defect matching, affine mapping, pseudo label generation and self-supervision consistency training are completed step by step, and fine-grained optimization is performed on dynamic change of a segmentation boundary. Therefore, the accuracy of defect detection is improved, and high-quality data support is provided for production process improvement and defect traceability.

The invention discloses a defect detection method and system for prompting distillation based on a thermodynamic diagram, and the method comprises the steps: collecting and preprocessing a plurality of product surface pictures, and constructing a defect detection data set; constructing a teacher model and a student model, training the teacher model, and storing the weight of the trained teacher model; constructing a thermodynamic diagram prompt module and embedding the thermodynamic diagram prompt module into the teacher model to obtain a new teacher model, training the thermodynamic diagram prompt module in the new teacher model, and storing the weight of the trained new teacher model; respectively inputting the product surface picture into a new teacher model and a student model, calculating distillation loss according to multi-scale features output by the new teacher model and the student model, and updating the weight of the student model in combination with the distillation loss and the detection loss of the student model to obtain a trained student model; and deploying the student model into the equipment terminal and carrying out defect detection to obtain a detection result. Defect detection is carried out in a deep learning mode, and manpower and material resources are greatly saved.

The embodiment of the invention discloses a building engineering crack detection method and system based on image recognition, and the method comprises the steps: obtaining a building surface image, carrying out the preprocessing of the image, obtaining a standardized image, and carrying out the multi-scale decomposition extraction and integration of various features, and forming a multi-dimensional feature descriptor set; after feature importance is evaluated, a compact feature vector is generated through dimension reduction, quantization coding and compression, and then a multi-level feature index mechanism for optimized compression is constructed. A query feature vector is extracted from a newly collected image, searching and screening are completed by means of an index mechanism and a tolerance threshold, and a crack matching result is obtained; and based on the result, positioning cracks, classifying types, measuring parameters and evaluating severity, and generating a crack state report. The crack trend is analyzed in combination with the historical data time sequence, a multi-stage early warning mechanism is designed, maintenance suggestions are provided, and a real-time monitoring and early warning system is formed. According to the embodiment of the invention, the technical problems of high storage pressure and low real-time detection efficiency in the prior art can be effectively solved.

The invention discloses a bridge crack feature fine quantification method and system based on deep learning, and relates to the technical field of bridge detection, and the method comprises the steps: carrying out the preprocessing of bridge surface image data; inputting the preprocessed image data into a U-Net network fused with a multi-scale channel space attention module for semantic segmentation and outputting a crack segmentation mask graph; performing morphological refinement operation to obtain fracture skeleton line data, and calculating a trend angle and curvature distribution; measuring the crack width along the normal direction of the skeleton point to generate a width distribution thermodynamic diagram; accumulating skeleton point intervals to calculate the total length and identifying branch features; according to the method, the segmentation IoU reaches 85% or above, the width precision is superior to 0.05 mm, and the method has the crack development trend prediction capability.

The invention relates to the field of power transmission line device defect detection, and discloses a strain clamp defect detection method and device based on computer vision, and the method comprises the steps: obtaining a global three-dimensional point cloud model of a strain clamp; mapping a standard detection path to the model; synchronously acquiring an internal structure image, an external surface image and an acquisition pose along the path; identifying suspected defects in the internal structure image; if the defect exists, calculating a corresponding associated influence area of the defect on the three-dimensional model by combining the pose; and performing enhanced scanning on the associated influence area to obtain high-precision information. The device comprises a ray machine; a ray machine control module; a digital ray imaging detector; a multi-dimensional information fusion probe; a wireless transmission module; and a power module. According to the method, internal and external defects can be accurately associated, the detection process is standardized, the detection accuracy and repeatability are improved, and a traceable digital file is constructed.

The invention relates to the technical field of image processing, in particular to a concrete member surface defect detection method and system based on image segmentation, and the method comprises the steps: obtaining a surface image of a concrete member, and dividing the surface image into a plurality of image blocks; and performing frequency domain transformation on any image block to obtain a power spectrum. According to the method, the feature space period of each image block is analyzed and calculated through frequency domain transformation, and adaptive weighted fusion is carried out on texture features at different distances by using Gaussian weight on the basis of the feature space period. According to the method, the feature extraction process can dynamically adapt to the physical scale of image local textures, namely, small distance analysis is automatically emphasized on fine textures and large distance analysis is automatically emphasized on rough defects, so that scale-perceived composite texture features are constructed; and the accuracy of identifying the concrete surface defects under the complex texture background is obviously improved.

The invention provides a tunnel lining surface crack detection method and system, and relates to the technical field of tunnel engineering, and the method comprises the steps: obtaining a lining surface image and a crack label of a detected tunnel, and a lining surface image of a to-be-detected tunnel; performing feature extraction on the lining surface image based on a visual cell mechanism to obtain perception feature maps of the detected tunnel and the to-be-detected tunnel; embedding mapping and contrast learning constraint are carried out through the perception feature map of the detected tunnel and the crack label, and a crack embedding space is obtained; mapping the perception feature map of the tunnel to be detected to the crack embedding space, and performing distribution alignment on the detected tunnel embedding features and the tunnel embedding features to be detected in the crack embedding space through a confrontation mechanism; and generating a lining surface crack detection result of the to-be-detected tunnel through the aligned crack embedding space. According to the method, the problem that cracks and non-cracks are difficult to stably distinguish due to inconsistent crack feature distribution in different tunnel scenes in an existing detection method is solved.

The invention discloses a concrete multi-scale apparent defect lightweight detection method, system, equipment and medium, and relates to the technical field of computer vision and deep learning, and the method comprises the following steps: obtaining a to-be-detected concrete surface image, preprocessing, and inputting a lightweight defect detection model; extracting a multi-scale feature map through a feature extraction network by adopting a lossless down-sampling operation, retaining original detail information, inputting the feature map into a feature fusion network, fusing and generating a feature map by utilizing a spatial context attention mechanism, outputting and analyzing through a detection head, obtaining defect type and position information, and generating a visual detection result. According to the method, through lossless down-sampling, multi-scale feature fusion, a space context attention mechanism and end-to-end lightweight design, the detection rate of small defects and the discrimination capability under a complex background are remarkably improved, meanwhile, the generalization and the real-time performance of the model are considered, and efficient and reliable technical support is provided for concrete apparent defect detection.

The invention relates to the technical field of underwater facility measurement, and provides an underwater facility defect geometric feature measurement method and system, a medium and equipment, and the method comprises the steps: calculating the actual sound velocity based on the water temperature, water pressure and salinity, correcting a sonar point cloud, calculating the sound velocity change rate, and adjusting the size of a filtering window, performing smooth filtering on the corrected sonar point cloud; for the surface image, calculating an image dark channel, after smoothing the dark channel image through scattering filtering, calculating transmissivity, fusing a scattering coefficient, removing surface image scattered light, and correcting the scattering coefficient through turbidity; based on the salinity and the water temperature, the underwater refraction angle of the laser is calculated, and the three-dimensional scanning point cloud is corrected; registering and fusing the point clouds to generate a facility surface point cloud model; based on the surface image, texture information of the facility surface is extracted, after the texture information is mapped to the facility surface point cloud model, a defect area is segmented, and geometric feature parameter extraction is carried out. And the detection reliability in a complex water area environment is improved.

The invention discloses an automobile casting multi-equipment detection data fusion judgment method, relates to the technical field of intelligent detection, and solves the problems that in an existing detection method, confidence output of multi-modal data is incomparable, misinformation is easily caused by evidence conflicts, and positioning offset is generated due to the fact that multi-modal registration is limited by a rigid model. According to the technical scheme, the method comprises the steps that surface images, point cloud and X-ray projection data are preprocessed, a detection response matrix is established, probability mapping is conducted on each modal score to form likelihood distribution and uncertainty representation, weighted fusion is completed in combination with a redundancy coefficient matrix, and a conflict judgment mark is generated; on the basis, hierarchical registration is executed, and a final judgment and recheck label is output; based on the technical scheme, the judgment accuracy and stability of multi-source detection data fusion of the automobile casting are remarkably improved.

The invention discloses a workpiece surface defect image recognition method, and relates to the technical field of defect recognition. The method comprises the steps that numerical control machining parameters are collected, a contact area force field cloud picture is generated through calculation, the cutting process of a tool is simulated, and a predicted texture image is generated; collecting a qualified workpiece surface image, calculating a pixel mean square error between the qualified workpiece surface image and the predicted texture image, and calibrating the predicted texture image through a least square method; collecting a to-be-detected workpiece surface image, comparing the to-be-detected workpiece surface image with the calibrated and predicted texture image, and determining a potential defect area; performing feature enhancement and extraction on the potential defect area image to obtain a defect standardized feature vector; and building a defect type identification model based on a support vector machine, inputting a defect standardized feature vector, outputting a surface defect type and binding a coordinate, and generating a surface defect coordinate and type. And on the basis of the surface defect coordinates and types, recognizing the surface defects of the workpiece.

The invention discloses a concrete apparent defect detection method based on image recognition, which comprises the following steps: firstly, collecting concrete surface images with cracks, spalling and other defects, marking and classifying, establishing a data set, and dividing the data set into a training set, a test set and a verification set in proportion; secondly, a DAPF-YOLO detection model is constructed, a YOLO11 model is used as a basic framework, and an input layer, a backbone network and the like are included; the backbone network adopts a double-branch context sensing backbone network to enhance the multi-scale feature extraction capability; the feature fusion layer adopts an AFTRep module to replace an original SPPF module to realize adaptive feature fusion, and adopts a CSP-PAFNet module to improve the multi-scale feature aggregation capability; reFSC Head is used to reduce the amount of parameters and enhance feature expression. And then, training the model by using the training set, monitoring the training through the verification set, and evaluating the performance by using the test set. And finally, inputting a to-be-detected image into the trained model, and outputting defect category and position information.

The invention discloses a tunnel cross-sectional image analysis method based on image processing, and aims to solve the problems that a surface image is not clear in correspondence with a transient seismic wave method, a geological radar and a resistivity imaging section space, and anomalies at a certain distance in front are difficult to map to a tunnel face. According to the method, anisotropic reforming is carried out by adopting a Fourier neural operator, cross-modal Transform registration of a micro attention mask based on sector geometry, curve mileage and section polar coordinate position coding is combined, analytic geometry mapping and uncertainty propagation are matched, tunnel face structure traces and water seepage texture evidences are fused, and the tunnel face structure traces and the water seepage texture evidences are combined. The technical effects of accurate positioning of abnormity on the tunnel face, position confidence range estimation, risk grading early warning, stripe artifact suppression, abnormal boundary reservation and output of structured results of mileage stake numbers, azimuth angles, distance intervals and the like are achieved.

The invention discloses an unmarked steel rail surface defect screening method based on self-supervised learning, and relates to the technical field of steel rail maintenance. Comprising the following steps: S100, acquiring steel rail surface image data and carrying out data preprocessing to generate an enhanced image pair; s200, constructing a defect screening basic feature encoder through a multi-scale visual pre-training model, and generating a final multi-scale fusion feature vector based on the enhanced image pair; and S300, constructing a dynamic pseudo tag generation unit, and calculating the cosine similarity between the final multi-scale fusion feature vector and the nearest neighbor normal sample feature vector. According to the method, a multi-scale visual pre-training framework is constructed, deep visual features representing the normal state and the abnormal state of the surface of the steel rail are automatically learned from massive original steel rail images on the premise that manual labeling is not needed, and a dynamic pseudo-label generation mechanism and a cross-scene migration adaptation unit are combined, so that the real-time performance of the system is improved. High-precision automatic screening of steel rail surface defects is achieved, and the generalization ability of the model in a complex environment is improved.

The invention discloses a rubber surface defect real-time detection system and method based on a knowledge graph, and relates to the technical field of rubber product quality detection. Comprising the following steps: S1, acquiring a high-resolution image of a rubber surface in real time; and S2, based on the high-resolution image, visual features of potential defects are extracted by using a deep learning model, and the visual features comprise texture, color, shape, size and position information. Preprocessed high-resolution rubber surface images are obtained in real time through the high-speed camera and the stable light source, and the problems that traditional manual detection is low in efficiency and prone to being influenced by subjective factors, and missed detection is caused are solved; multi-dimensional visual features such as textures and colors are extracted by using a deep learning model, mapping of visual features and semantic concepts is established through logical reasoning and semantic fusion in combination with a knowledge graph in which defect semantic knowledge is stored by a triple, and the defect that existing computer visual detection lacks deep semantic understanding is made up.

The invention discloses an unmanned aerial vehicle wind power blade defect dynamic detection method based on AI vision, and relates to the technical field of JSLYMC, and the method comprises the following steps: S1, collecting an original dynamic image sequence; s2, generating an aligned image sequence; s3, generating a standardized blade surface image sequence; s4, constructing a blade topological structure diagram; s5, inputting the graph structure data into the improved TransGAT model, and outputting a defect candidate set; s6, inputting the defect candidate set into the FairMOT model, and outputting a defect space-time trajectory; and S7, generating a structured detection report. The method overcomes the limitations of dependence on manual inspection, poor dynamic adaptability and insufficient identification precision in a traditional wind power blade defect detection method, and provides an efficient and accurate solution for unmanned aerial vehicle wind power blade automatic inspection and intelligent maintenance decision.

The invention provides a loudspeaker mask defect identification method and system based on visual inspection, and relates to the technical field of machine visual inspection, and the method comprises the steps: collecting a surface image of a loudspeaker mask through an industrial camera, calculating a median gray value in a pixel neighborhood to effectively filter out impulse noise, carrying out size standardization processing, and obtaining an image of the surface of the loudspeaker mask; a standardized image is obtained; according to the standardized image, a convolutional neural network is adopted, and a parameter learning rate is dynamically adjusted through an adaptive moment estimation mechanism, so that multi-scale feature mapping of mask textures is extracted; according to the multi-scale feature map, candidate defect regions are generated through a region suggestion network; and for a candidate defect area proposal, using a U-Net network and a learning rate to quickly approach an optimal solution, attenuating the learning rate according to an exponential law along with the increase of the number of iterations to stably converge to fine local optimum, and performing pixel-level fine segmentation on a defect boundary to obtain a defect mask image. According to the invention, the detection efficiency of the loudspeaker mask is improved.

The invention provides a workpiece surface target merging and partitioning method, system and device, and relates to the technical field of image processing, and the workpiece surface target merging and partitioning method comprises the steps: obtaining a surface image of a target region of a target maintenance workpiece; performing graph sampling and visual processing on the surface image to generate a binary image of the target area, and analyzing the binary image to obtain a plurality of connected domains of the target area; performing convex hull generation according to the connected domains to obtain a convex hull corresponding to each connected domain; generating a rotation enclosing rectangle set according to the leading edge vector of each convex hull; and through a variant ant colony optimization algorithm, carrying out global optimization on the rotating enclosing rectangle set, and generating a combined partition of the target area. According to the invention, through accurate capturing of the original image and subsequent layer-by-layer optimization, the redundancy of the laser maintenance path is reduced, and the operation cost is reduced; through construction and global optimization of a high-adaptation form carrier, precise coverage of a complex form target is realized, and the maintenance precision is improved.