3186 results about "Edge detection" patented technology

The invention provides a circuit board welding fault identification method and system based on machine vision, and relates to the technical field of visual identification, and the method comprises the steps: obtaining an image of a circuit board welding region through employing a structured light projection system and a multi-camera imaging system; realizing accurate positioning of a defect region in the region of interest by utilizing three-dimensional shape information and through morphological operation and an edge detection algorithm; for the accurately positioned defect area, obtaining high-precision three-dimensional point cloud data of the defect area, extracting a multi-scale curvature feature descriptor, and fusing the multi-scale curvature feature descriptor with visual features to form a multi-feature descriptor; inputting the multi-feature descriptors into a defect classification and recognition module based on ensemble learning to obtain the types and severity of the welding defects; and according to a defect identification result, the three-dimensional space distribution and severity of the welding defects are visually displayed.

The invention relates to the technical field of image processing, and discloses an industrial article appearance detection method and system based on machine vision. The method comprises the steps that a workpiece image is collected, and a standardized image is obtained through Gaussian filtering, histogram equalization and Otsu algorithm preprocessing; feature vectors are extracted through edge detection, Hough transform and a gray-level co-occurrence matrix; training a defect identification model by adopting a CNN of an attention mechanism; inputting an image for real-time detection; fusing the detection data and the environmental parameters by using a D-S theory; and establishing a database based on a distributed architecture to generate a quality report. According to the method, the detection accuracy is improved through multi-source information fusion, and efficient management and analysis of mass detection data are realized by adopting a distributed architecture.

The invention relates to the technical field of image processing, and discloses a stranded wire production quality monitoring method and system based on machine vision. The method comprises the following steps: acquiring the surface of a stranded wire at multiple angles through a high-speed camera array to obtain an original image; filtering high reflection and noise to obtain a feature map; performing edge detection, segmenting and identifying defects, and generating a position mapping table; quantitatively calculating defect parameters to form a quality score; performing correlation analysis to adjust process parameters; and constructing a defect-process database to realize quality tracing early warning. According to the invention, full-process automatic control from defect identification, quantitative evaluation to process parameter optimization is realized, the defect-process relational database is constructed, and the tracing and early warning functions of production quality are supported, so that the quality stability and the intelligent level of stranded wire production are improved.

The invention discloses an electric power system abnormal remote signaling detection method based on a graph auto-encoder model, and the method comprises the steps: collecting measurement data of an electric power system, carrying out the preprocessing, obtaining a graph data set with an abnormal label, and dividing the graph data set into a training set and a test set; inputting the graph data in the training set into the graph auto-encoder model for training; after training is completed, abnormal score distribution is counted based on normal edge samples in a training set, a threshold value is set to serve as a follow-up judgment basis, and threshold value selection takes the accuracy rate and the recall rate on a test set as an adjustment and optimization target; in a test stage, image data in a test set are input to carry out edge feature reconstruction and anomaly scoring, anomaly judgment is carried out on edges in combination with a set threshold value, and a preliminary abnormal edge detection result is output; and the output abnormal edge detection result is input into the graph restoration module, the restored edge structure and edge features are output, the damaged remote signaling state in the power grid is restored, and the integrity of the graph structure and the operation credibility of the power system are improved.

The invention relates to the technical field of typesetting and drawing, in particular to an automatic typesetting and drawing method and system for lens laser cutting. The method comprises the following steps: collecting an image of a to-be-cut lens, carrying out contour edge detection, generating a contour direction sequence, carrying out polygon fitting on the sequence, screening out effective contour data, constructing a three-dimensional lens model, carrying out classification clustering on the model to extract contour features, carrying out segmentation to obtain a segmented contour model, and carrying out image segmentation on the segmented contour model. The method comprises the steps of obtaining a model, carrying out range limitation and local typesetting processing on the model to form an accurate layout scheme, constructing a cutting path direction chain according to the scheme, carrying out path planning, generating cutting path data, carrying out sequential execution setting on cutting paths based on a preset blank drawing template, and finally obtaining a typesetting template for laser cutting. The automatic typesetting and drawing method for lens laser cutting is more efficient.

The invention relates to the technical field of computers, and discloses a cultivated land resource quality grade evaluation management method and system, and the method comprises the steps: carrying out the spectrum residual error normalization processing of a multi-temporal remote sensing image, generating a corrected image sequence, effectively inhibiting the reflectivity difference caused by the alternation of wetting and drying of a water body, and achieving the evaluation of the quality grade of cultivated land. A first boundary result and a second boundary result are respectively extracted by using depth segmentation and progressive edge detection, the adaptability and robustness of boundary extraction are enhanced, a difference graph is further generated based on a complex phase difference matrix, the second boundary is corrected, a coherent boundary result is obtained, the boundary time sequence consistency is remarkably improved, and the robustness of boundary extraction is improved. And finally, the cultivated land parcel units are constructed based on the coherent boundaries, and the random forest model is input to complete scoring and grading, so that the stability and traceability of the cultivated land quality evaluation result under multiple temporal conditions are ensured, and the technical targets of stably extracting the boundaries and accurately completing grading in a dynamic change scene are achieved.

Presented herein are systems and methods for the employment of machine learning models for image processing as may be performed by computing devices associated with an end user. A method may include obtaining video data comprising a plurality of frames including a document of a document type. The method may include executing an object recognition engine of a machine-learning architecture using image data of the plurality of frames, the object recognition engine trained to detect edges of documents. The method may include identifying, based on the edge detection, a plurality of boundaries for the document. The method may include validating, based on the plurality of boundaries, the document as the document type. The method may include transmitting via one or more networks, to a computer remote from the computing device, responsive to the validation of the type of document, the image data for the plurality of frames depicting the document.

The invention discloses an efficient 3D Gaussian scene reconstruction method based on multi-modal depth distribution supervision, and compared with the prior art, pixel-level depth dense point clouds are directly extracted in a Gaussian initialization stage, a multi-modal depth distribution map is generated, random initialization of dense Gaussian positions according to COLMAP sparse point clouds is effectively avoided, and the reconstruction efficiency is improved. The problems that under the condition of few views, point clouds obtained by COLMAP are too sparse, provided geometric constraints are insufficient, and meanwhile randomly initialized Gaussian blocks the point clouds to move to the correct position due to the local minimum value are solved. According to the method, an image segmentation algorithm based on edge detection is applied to the image frame, a large number of Gaussian primitives which are far away from edge points and are similar in depth are fused, the 3D Gaussian number is effectively pruned, and memory consumption of a GPU is saved. According to the method, extra isotropic regularization loss is introduced into global gauss, so that the problem of Gaussian distribution in which artifacts are generated along the line-of-sight direction and the height is lengthened is avoided.

The invention provides a water area safety patrol method and system based on multi-modal feature analysis, and relates to the technical field of image recognition. The method comprises the steps of collecting a visible light image, an infrared image and laser radar data corresponding to a target water area, and performing time synchronization and space alignment to obtain visible light alignment data, infrared alignment data and laser radar alignment data; in response to the condition that the definition of the visible light image is higher than a quality reference value, carrying out edge detection and region segmentation processing on the visible light alignment data, and generating a candidate region set containing a target contour; for the infrared alignment data corresponding to the candidate region set, screening out a thermal anomaly region from the candidate region set; and carrying out three-dimensional space feature extraction and motion state feature extraction on the laser radar alignment data corresponding to the thermal anomaly region, and carrying out anomaly identification on the thermal anomaly region. According to the scheme, high-precision safety patrol of the target water area in various environments can be realized.

The invention discloses a virtual water gauge water level identification method based on a multi-mask matching segmentation network. The method comprises the steps that S1, a real-time image of a target water body area is acquired through a preset position camera; S2, a virtual water gauge geometric model is constructed; s3, extracting a region with high confidence as a candidate region of the segmentation model; s4, designing a new training data structure optimization method to help the model better understand and utilize information of different levels; s5, respectively carrying out maximum matching on the rows and the columns, finding out an optimized matching path with the minimum total cost, and obtaining a finer segmentation result; s6, identifying a water body boundary in the high-precision water body segmentation map through a Canny edge detection algorithm, and extracting a waterline position; and S7, calculating an actual water level value, and for the water body with dynamic movement, smoothing the water level reading by adopting an interval time sampling moving average method. The defects of a traditional physical water gauge and an existing image processing technology are overcome, and an innovative solution is provided for water level monitoring.

The invention discloses a double-branch edge detection method and system based on CNN and Transform deep interactive fusion, and relates to the technical field of computer vision and deep learning, and the method comprises the steps: extracting multi-scale local features through a CNN-based fine semantic edge branch by employing an OfficientNet-B2 backbone network, and carrying out the deep supervision in combination with a side output structure; meanwhile, through a global context branch based on lightweight Transform, an efficient local attention mechanism is adopted to capture a long-distance dependency relationship of the image; the core of the invention lies in that a cross-attention module is designed and realized, local features extracted by a CNN branch are used as a Key and a Value, and features of a Transform branch are used as Query, so that deep interaction and fusion of the two branches on the feature level are realized, instead of simple feature splicing; in addition, the method also adopts a uniform loss function for balancing dynamic weighted combination of binary cross entropy loss, focus loss, Dice loss and the like to perform model optimization.

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

The invention discloses a plant disease and insect pest early warning method and system based on plant monitoring, and belongs to the field of plant disease and insect pest early warning, and the method comprises the steps: extracting the contour of a lesion region through an edge detection algorithm, carrying out the smoothing of the contour through the combination of morphological operation, and obtaining a more precise lesion region boundary; according to an image segmentation result and a disease type identification result, a plant health condition evaluation model is established, and the plant damage degree is quantified; environmental data and image data are acquired from a plurality of sensor nodes distributed in a field, and the data are gathered to a regional gateway through wireless transmission; carrying out preprocessing and feature extraction on the converged heterogeneous data in a regional gateway, removing noise data and redundant information, and extracting key features; and carrying out pest detection and counting on the preprocessed image by using a deep learning model, carrying out modeling on a pest number change trend, predicting population density change in a period of time in the future in combination with environmental factors, and generating a detection result.

The invention relates to the technical field of image processing, and discloses a precast concrete T-beam bubble detection method and system. The method comprises the following steps: carrying out multiband reflection data acquisition and calibration on the surface of a T-beam, and obtaining optimized data through light intensity compensation and band screening; performing spatial registration and noise reduction processing on the data, and fusing the data into an image; edge detection and image segmentation are carried out, and pore morphological features are extracted; calculating depth data and performing spatial reconstruction to obtain pore distribution data; performing statistical analysis on the distribution data to obtain a defect evaluation result; and finally generating a detection report. According to the method, accurate positioning and quantitative evaluation of bubble defects are realized through multi-source data fusion and deep analysis, a complete early warning and evaluation mechanism is established, and the reliability and practicability of a detection result are improved.

The invention discloses a solar cell defect detection method based on a multi-mode sensing technology. The system comprises the following steps: 1, multi-modal data acquisition: acquiring multi-modal data in real time by using four sensing technologies of near-infrared laser, area array visible light, PL and EL; 2, multi-modal data processing: carrying out denoising, edge detection and feature extraction on the acquired near-infrared, visible light, PL and EL images by adopting a mixed image processing algorithm to obtain key defect features; 3, multi-modal data fusion: combining a multi-modal data fusion method with a decision-making level strategy, synthesizing different technical data, improving the detection precision, and classifying, positioning and labeling the defects of cracks, broken gates, scratches, depressions, poor welding and hot spots by using a convolutional neural network to ensure accurate distinguishing of surface and internal defects; and 4, defect marking and production line adjustment: marking defects and adjusting the production line through a feedback and automatic control module, and feeding back a real-time visual result to a management layer.

The invention relates to the field of excavation equipment obstacle avoidance based on image analysis, in particular to a coal mine excavation equipment obstacle avoidance method and system based on machine vision, and the method comprises the steps: obtaining the frequency domain characteristic parameters of the current electromagnetic noise during the operation of underground electromechanical equipment, and synchronously collecting the current original image frame output by a visual sensor; after a current de-noised image frame is obtained after dynamic de-noising processing, an obstacle contour feature point set is extracted through multi-scale edge detection, and an obstacle position confidence map is obtained according to space-time consistency analysis; obtaining point cloud coordinates of an image data loss area caused by electromagnetic interference, and mapping the point cloud coordinates to an image coordinate system to obtain a three-dimensional geometric complemented image; and constructing a dynamic occupation grid map of the underground environment according to the three-dimensional geometric complementation image and the obstacle position confidence map, and planning an obstacle avoidance path of the mining equipment according to the dynamic occupation grid map. And the obstacle avoidance capability of the mining equipment is improved, and safe and efficient coal mining operation is guaranteed.

The invention relates to the technical field of remote sensing data processing and glacier area calculation, in particular to an unmanned aerial vehicle and satellite remote sensing data fused glacier area calculation method, which comprises the following steps of: cooperatively acquiring a satellite multispectral image and unmanned aerial vehicle high-resolution optical and LiDAR data, performing time synchronization, high-precision space registration and data enhancement processing, and calculating the glacier area through the unmanned aerial vehicle and satellite remote sensing data fusion. A satellite image glacier macroscopic feature and an initial mask are extracted by using a convolutional neural network and an NDSI / NDWI algorithm, and unmanned aerial vehicle image microscopic texture, edge and topographic features are acquired through a local binary pattern, edge detection and LiDAR point cloud; based on pyramid layering and a conditional random field, adopting a variance weighting algorithm to realize multi-scale feature level fusion; and after segmentation through an Otsu algorithm, calculating the area through a pixel counting method and introducing gradient correction, and evaluating the reliability through three types of precision. The method breaks through the limitation of a single data source, fuses macroscopic and microscopic features, improves the boundary positioning precision and calculation efficiency, and is suitable for glacier dynamic monitoring in a complex environment.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The pattern generation method based on diffusion model fine tuning comprises the following steps: constructing a data set; the generation model comprises an edge detection model ControlNet and a stable diffusion model, the stable diffusion model comprises a text encoder CLIP, a diffusion model U-Net and an image decoder VAE, the CLIP text encoder converts an input natural language into word vector features and inputs the word vector features into a U-Net network in a submerged space, and the diffusion model U-Net and the image decoder VAE are connected with the edge detection model ControlNet and the diffusion model U-Net; the U-Net network is controlled to carry out iterative denoising on pure noise in a low dimension to generate a compressed image, and finally the compressed image is restored to an original pixel image through a decoder VAE, so that text content control image generation is realized; carrying out fine tuning training on the generative model by adopting a LoRA parameter efficient fine tuning technology; and intelligently generating a batik style pattern. The method has the advantages that the texture precision of the generated image can be improved, and the personalized style image can be generated.

The invention relates to the technical field of image recognition, in particular to an intelligent electric energy meter image collection method and system based on multi-source data, and the method comprises the following steps: collecting a multi-angle reflection contour image of an electric energy meter through a preset incident angle light source, and calculating a difference value point by point based on a standard reference contour point coordinate; and after edge sections are divided, the maximum value, the minimum value and the mean value are processed by adopting a Gaussian filtering algorithm, and a multi-angle difference statistical result is generated. According to the invention, through multi-angle reflection contour image acquisition, difference calculation and edge division, Gaussian filtering noise reduction, ultralimit edge segment extraction, angle focal length compensation through step length adjustment, edge extraction through a Canny edge detection algorithm and contrast change rate calculation, dynamic path instruction generation, high-frequency feature point density extraction through SIFT, and direction angle adjustment scanning sequence analysis, the method can be used for realizing multi-angle detection of the image. Calculating a standard deviation and a gradient variance to generate an abnormal graph, performing local focusing to compensate an abnormal region, performing gray scale consistency splicing replacement, and performing multi-source response optimization closed-loop correction.

The invention relates to the technical field of image processing, and discloses a film lamination defect detection method and system based on image processing. The method comprises the following steps: acquiring and preprocessing a film-conducting layer interface by using a polarization enhanced imaging device to obtain an enhanced image; applying double-layer gradient edge detection to extract the edge of a fitting area, and marking a micro-defect sensitive area; an interface smoothness index and a fitting uniformity feature are obtained through multi-scale morphology feature extraction; inputting the features into a support vector classifier to identify bubbles-wrinkles; performing three-dimensional reconstruction on an identification result to generate a bubble distribution and stress field graph; and finally evaluating the fitting quality, and outputting an integrity score. According to the invention, automatic accurate detection and quantitative evaluation of tiny defects of the thin film-conducting layer bonding interface are realized.

The invention relates to the technical field of image processing, in particular to a feature point automatic labeling method and system based on point cloud data. The method comprises the following steps: acquiring regional multi-source point cloud data, and performing multi-modal data fusion to obtain point cloud fusion data; performing shoreline structure ground point cloud segmentation based on the point cloud fusion data to obtain a ground point cloud segmentation data set; performing edge detection on the ground point cloud segmentation data set, and identifying geometric salient points according to an edge detection result so as to obtain a feature point candidate set; performing ground feature type classification based on the feature point candidate set to obtain a target classification data set; distributing a symbolic pattern for the target classification data set and binding a point cloud feature attribute to obtain a symbolic data set; and performing multi-level symbol dynamic adjustment and interactive labeling based on the symbolized data set to obtain a symbol dynamic view. The method is helpful for improving the accuracy and efficiency of symbolization expression of the shore feature points, and has strong interactivity.

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 discloses a building structure crack intelligent detection method, which comprises the steps of S1, constructing a multi-dimensional intelligent sensing array, and obtaining four-dimensional spatio-temporal data including vision, stress, vibration and temperature; s2, performing feature enhancement processing on the multi-modal data; s3, realizing cross-modal crack identification and positioning based on a space-time attention neural network; s4, adopting a sub-pixel edge detection and ultrasonic genetic inversion algorithm; s5, constructing a coupling dynamics prediction model; s6, establishing a dynamic threshold evaluation and multi-dimensional self-calibration mechanism; 0.05 mm micro-crack identification and 3mm depth detection precision are achieved and are improved by 50% compared with a traditional method, the detection accuracy under the complex environment is larger than or equal to 98% through the multi-modal data fusion and GAN enhancement technology, the RMSE is smaller than or equal to 0.03 mm through short-term prediction, the accuracy is larger than or equal to 85% through long-term prediction, double guarantees of a physical mechanism and data driving are established, and the detection accuracy is improved by more than or equal to 98% through the multi-modal data fusion and GAN enhancement technology. And the system has self-calibration, self-adaptive sampling and multi-modal fusion decision-making capabilities, so that the manual intervention cost is greatly reduced.

The invention discloses an image processing algorithm for pupil center real-time extraction and trajectory tracking in an eye closing state. According to the algorithm, from the angle of digital image processing, graying, contrast enhancement and smoothing processing are firstly carried out on an input eye video frame by frame, and then the boundary of a pupil area can be accurately extracted by adopting an adaptive threshold segmentation technology and combining morphological operation and a Canny edge detection operator. And then, accurate fitting of ellipse parameters is performed on the pupil contour by using a geometric distance-based least square method, so that center coordinates and area information of the pupil are obtained, and the accuracy and reliability of pupil monitoring in an eye closing state are remarkably improved. Through recording and analyzing pupil center position changes in a time sequence, real-time tracking of pupil movement tracks is realized. And finally, evaluating pupil light reflex (PLR) in an eye closing state before and after white light excitation by adopting baseline correction and smooth filtering processing. The method has the advantages of high robustness, adaptability to illumination variation, high real-time performance, high precision and the like.

The invention provides an electronic clinical medical assessment method and system for an ophthalmology diagnosis and treatment scheme, and relates to the technical field of medical information, and the method comprises the steps: 1, carrying out the multi-scale edge detection calculation of an input ophthalmology image-text mixed medical record image, and obtaining a medical record image; the method comprises the following steps: extracting a boundary contour of a text labeling area and a hand-drawn lesion schematic diagram through adaptive threshold segmentation and morphological closed operation, and establishing a coordinate mapping table containing text block circumscribed rectangular coordinates, geometric positions of key anatomical mark points of the schematic diagram and symbol spacing characteristics; 2, constructing a two-channel attention gating network based on the coordinate mapping table, fusing the semantic features of the text region and the morphological features of the schematic diagram through a dynamic weight distribution strategy, and generating a multi-modal fusion feature matrix with a spatial alignment relationship; according to the method, through multi-modal feature fusion, triangular verification region confidence regulation and control and three-dimensional topology modeling, analysis and structured output of ophthalmology image-text medical records are realized, and the spatial alignment of diagnosis and treatment information is improved.

The invention provides a road marking quality intelligent evaluation system based on image analysis, and relates to the technical field of road facility monitoring, and the system comprises a detection unit, a marking quality evaluation unit, a marking wear prediction unit, a GPS positioning unit, a vehicle driving information unit, a control unit and a remote central control unit. The marking quality evaluation unit is constructed based on a differential geometry theory and comprises a curvature flow edge detection module, a marking geometric characteristic manifold representation module and a multi-scale differential invariant evaluation module, the system regards a marking as a two-dimensional manifold, and the marking quality is evaluated by calculating differential geometric quantities such as a Gaussian curvature, an average curvature and a shape index. And constructing geodesic distance measurement on the feature manifold, and evaluating the completeness, visibility and reflective performance of the marked line. And the marking wear prediction unit predicts the service life of the marking based on the traffic flow information and the environment characteristic mapping relation model, and generates a maintenance suggestion.

The invention discloses an intelligent classification method and system for defects of a strain clamp of a power transmission line based on a two-dimensional image, and belongs to the field of defect classification of the strain clamp, and the method comprises the steps: carrying out the preprocessing of the two-dimensional image of the strain clamp, extracting related features, and constructing a defect region candidate set; the method comprises the following steps: firstly, acquiring edge information by using an edge detection algorithm, and calculating curvature and roughness to generate a morphological feature vector; then, training a support vector machine model based on the defect sample set, and classifying the first candidate set to obtain a first defect classification result; thirdly, density feature correction is carried out on wire clamp images of different materials, gray gradient features are extracted, a second candidate set is obtained, and the second candidate set is input into the trained SVM model for second defect classification; and finally, integrating the first classification result and the second classification result to generate a final defect classification result. According to the method, morphological characteristics and material characteristics are effectively combined, and the accuracy and robustness of defect detection are improved.

The invention relates to the technical field of candid shooting detection, and discloses a monitoring screen area candid shooting detection method based on real-time visual identification, which comprises the following steps: determining a screen area, judging whether a suspected candid shooting behavior exists or not according to an overlapping result of a current image and a previous image of acquisition equipment, identifying a gesture action of the screen image, the method comprises the following steps: determining an object contour of a screen image according to an edge detection algorithm, searching an object contour value in a preset object contour table, obtaining a lens brightness value according to effective lens pixel points, outputting a contour weight factor of the object contour value and a brightness weight factor of the lens brightness value by adopting a candid shooting behavior model based on a gesture action, and comparing the candid photographing behavior value with a candid photographing behavior threshold value, and judging whether to send candid photographing early warning information to the signal shielding device or not according to a comparison result. The candid photographing behavior is comprehensively measured through the object contour value, the lens brightness value and the gesture action, and the candid photographing detection reliability is improved.

The invention discloses an offshore wind power blade defect detection method, and belongs to the technical field of wind power blade detection. The detection method comprises the following steps: S01, offshore wind power blade defect detection: identifying the position of a blade defect, outputting a positioning mark frame of the defect on an original image, and providing the coordinate of the center position of the mark frame and the corresponding length and width; s02, leaf image splicing: carrying out step-by-step shooting; splicing each detected image by means of an image splicing technology to form a complete fan blade image; and S03, determining the actual physical position of the blade defect in the single image: in the spliced total image, determining the actual physical coordinate of the blade defect in the original single image corresponding to the fan blade. According to the detection method, multiple detection means such as computer vision, feature extraction, image splicing and edge detection technologies are fused, blade surface defect information can be comprehensively obtained, and the detection precision and accuracy are greatly improved.