21472 results about "Image based" patented technology

The invention discloses a high-precision image processing method and system based on illumination adaptive compensation, and relates to the technical field of computer vision and image processing, and the method comprises the steps: inputting an original image, and dividing the image into a high-frequency edge layer, an intermediate-frequency texture layer and a low-frequency illumination layer through a multi-scale residual network; acquiring illumination intensity, color temperature and scene categories in real time by using an ambient light sensor and a scene semantic segmentation model, and generating dynamic compensation parameters; carrying out dynamic range expansion on a low-frequency illumination layer based on a physical illumination model, and adjusting the weight of highlight suppression and dark area enhancement through a self-adaptive S-shaped exposure curve; a double-branch generative adversarial network is adopted, noise suppression and super-resolution reconstruction are carried out on the high-frequency layer, and texture detail enhancement is carried out on the intermediate-frequency layer; aligning the data of the depth camera and the infrared sensor with the visible light image through a cross-modal fusion module; and performing tone mapping on the fused image based on human visual characteristics, and outputting an enhanced image with a high dynamic range and reserved details.

A method includes obtaining an image, the image associated with a mask corresponding to a portion of the image, generating a plurality of images based on the image and the mask, each image of the plurality of images depicting a different color in the portion of the image corresponding to the mask, executing a machine learning model to generate an image performance score for each of the plurality of images, ranking the plurality of images according to the image performance scores for the plurality of images, and generating a record comprising one or more images of the plurality of images based on the rankings of the plurality of images.

The invention relates to the field of defect detection, in particular to a PCB defect detection method and system based on image recognition. The method comprises the following steps: collecting a multidirectional PCB detection image, carrying out pixel-level registration correction and adaptive pixel stability compensation, and constructing a space-time stability compensation image sequence; performing reverse pyramid structure division on the space-time stability compensation image sequence, performing normalized similarity probability calculation, and constructing an initial region classification result; based on an initial region classification result, depth image visual analysis is carried out, pseudo defect comprehensive elimination optimization is carried out, and a pseudo defect purification high-confidence image is constructed; pCB connection defect identification is carried out on the pseudo defect purification high-confidence image, global defect point distribution marking is carried out, and a defect point space distribution diagram is constructed. According to the invention, high-credibility, high-precision and high-closed-loop PCB defect detection is realized.

In a measurement processing apparatus for measuring a geometric feature of an object image: a measurement-reference-element setting unit automatically sets at least one first measurement reference element for use in measurement of the geometric feature of the object image, at at least one first position on the object image based on first image data representing the object image and position information indicating at least one second position of at least one second measurement reference element which is set on a measurement reference image corresponding to the object image; and a geometric-feature measurement unit measures the geometric feature of the object image based on the at least one first position of the at least one first measurement reference element.

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

The invention relates to the technical field of image processing, and discloses a backlight effect image edge enhancement method based on intelligent identification, which comprises the following steps of: judging a field environment type; performing global optimization on the original image based on a set environment perception type enhancement mechanism according to the judged field environment type, and outputting a global pre-processing image; constructing a backlight area segmentation model for the globally preprocessed image; outputting a local enhanced image; designing a structure perception type local adaptive threshold algorithm for the local enhanced image, and outputting a binary image keeping structural continuity; extracting an edge image of the binarized image through an edge detection algorithm, and optimizing a topological structure of a contour in the binarized image; and comparing with a wood template processing standard feature library, outputting an edge quality evaluation result and feeding back to a processing control system. Defect detection and machining control depth linkage is achieved, passive detection is changed into active optimization, and the production efficiency and the yield are improved.

The invention discloses an unmanned aerial vehicle electric power inspection image intelligent analysis method and system based on deep learning and multi-modal fusion and a medium thereof, and relates to the technical field of electric power equipment detection. The method comprises the following steps: planning an optimal inspection path by adopting an A * algorithm to realize multi-sensor synchronous data acquisition; adaptive histogram equalization and defogging processing are carried out on the visible light image, non-uniformity correction and temperature calibration are carried out on the infrared image, and filtering and registration are carried out on point cloud data; constructing a multi-scale feature fusion network based on improved VGGNet-16, and introducing deformable convolution and a cross-modal attention mechanism to realize multi-source data fusion; defect detection is carried out based on a three-level template library and a feature map cross-correlation algorithm, and the precision is improved in combination with non-maximum suppression and sub-pixel positioning; and finally generating a detection report containing defect types, positions and maintenance suggestions. According to the invention, the automation level and the detection precision of power inspection are obviously improved.

The invention relates to the technical field of hydraulic engineering safety monitoring, and particularly discloses a hydraulic engineering potential safety hazard assessment and prediction system and method based on image recognition. A multi-scale convolutional neural network is combined with a three-dimensional point cloud registration technology to extract surface visual feature parameters, and adaptive time-frequency analysis and a wavelet packet reconstruction algorithm are used to extract physical feature parameters of internal concealment defects; constructing a dual machine learning framework, eliminating environmental interference through a deep residual network, analyzing a causal relationship between features based on a gating cycle unit, and screening a key feature parameter set; a Gaussian process regression model of an adaptive kernel function is used for dynamic risk prediction, risk abrupt change points are identified in combination with multi-scale wavelet transform, and finally a safety state score and a grading early warning signal are generated through a fuzzy comprehensive evaluation algorithm.

The invention relates to the technical field of computer vision detection, in particular to an unmanned aerial vehicle target detection method based on frequency-space joint attention and dynamic fusion, and the method comprises the steps: obtaining an unmanned aerial vehicle image data set, carrying out the preprocessing, and dividing a training set and a test set; constructing a target detection model, inputting the training set into the target detection model to extract image features, sequentially performing frequency domain detail enhancement, spatial domain salient region extraction and multi-scale feature adaptive fusion based on the image features, and establishing a feature sequence; screening the feature sequence to obtain an initial target query, and finishing target classification and positioning on the initial target query through a decoder; training a target detection model by using the training set, and inputting the test set into the trained target detection model to generate a detection result; on the premise that the real-time reasoning advantage of RT-DETR is kept as much as possible, the problems that in an unmanned aerial vehicle scene, a target is prone to missing detection, the scale change is large, the background is complex, and the target is fuzzy are effectively solved, and the detection precision is improved.

The invention discloses a bridge crack identification and automatic evaluation method based on image identification and AI modeling, and the method comprises the following steps: S1, obtaining an original image of a bridge structure surface, and carrying out the image preprocessing; s2, inputting the standardized image into an image recognition model, performing pixel-level segmentation on a crack region in the image, and outputting a crack mask graph; s3, performing feature extraction processing on the crack mask graph, extracting geometric feature parameters of the crack, and constructing a crack feature vector; s4, constructing an evaluation model based on a supervised learning method, and training the evaluation model; and S5, inputting the crack feature vector into an evaluation model, evaluating the structural risk level of the crack, and outputting a structural risk label. According to the method, image recognition and AI modeling are fused, automatic crack recognition and evaluation are achieved, and the method has the advantages of being high in precision, clear in boundary and intelligent in evaluation.

A human-guided vision-force fused impedance iterative learning control method for a robotic arm, comprising: analyzing a robot-environment interaction dynamics equation, solving a visual servo acceleration model, and making use of the equation to establish a human-robotic arm-environment interaction dynamics model in an image feature space; acquiring an image feature position and speed curve of a human-guided robot completing an assembly task, and using dynamic movement primitives for coding and generalization; and designing an impedance iterative learning controller which uses image feature tracking errors as control input, learning impedance characteristics when the human-guided robot performs a contact operation, identifying unknown contact dynamics under the interaction between the robot and the environment, and counteracting identified contact interference in the feature space, so as to implement a flexible assembly operation. The control method solves the problems in existing assembly operations that human-robotic arm-environment coupling nonlinear dynamics, unknown contact dynamics of intensive contact assembly tasks and poor generalization of assembly scenarios require relearning for different scenarios, etc.

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 an egg surface microcrack detection system and method based on image analysis, and relates to the field of surface defect nondestructive detection.The method comprises the steps that a process identification interface module obtains a processing process identifier of an egg in real time; the multi-angle annular light source module is provided with a multi-band annular light source array comprising visible light and near-infrared LED lamp beads which are independently controlled, a polaroid group and a beam splitter prism are integrated, and visible light polarization images and near-infrared polarization images on the surfaces of the eggs are synchronously collected; the thermal excitation enhancement unit obtains a thermal infrared image; the process filtering module extracts the axial elongation of the mechanical stress microcrack after graded transportation, and calculates the mesh fractal dimension of the thermal stress microcrack after UV disinfection; the multi-modal feature fusion module generates a dual-channel crack probability graph; and the dynamic feedback control module outputs a micro-crack risk grade and adjusts the rotating speed of the objective table and the light source intensity. The method has the advantages that accurate judgment of mechanical and thermal stress cracks is realized, and curved surface reflection interference is broken through.

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

The invention relates to the technical field of unmanned aerial vehicle flight attitude control, in particular to an unmanned aerial vehicle flight attitude adjustment method and system based on reinforcement learning. Comprising the following steps: constructing a dynamic environment grid map based on a digital map and a real-time semantic segmentation result, and generating an initial track by introducing a space-time constraint fast search random tree algorithm; collecting flight state information, environment perception information and image definition indexes of the unmanned aerial vehicle, inputting the flight state information, the environment perception information and the image definition indexes into a space-time attention encoder, and generating a semantic-fused state tensor; performing importance distribution on the state tensor through an information entropy weighting mechanism to obtain a weighted state vector; inputting into a Meta-SAC model with meta-learning ability, and outputting a target flight reference pose and an LQR controller dynamic gain coefficient; the control module drives the LQR controller to generate a flight control instruction based on the information; and a reward function is constructed based on the image quality and the energy consumption efficiency, and a reinforcement learning strategy is fed back in real time.

The invention relates to the technical field of production line defect detection, in particular to an intelligent packaging production line defect detection method and system based on an image recognition model. The method comprises the following steps: carrying out packaging container surface defect analysis on an empty packaging container to generate a container inherent defect area; capturing a disturbance response time sequence image sequence based on the inherent defect area of the container after the liquid product packaging operation of the empty packaging container is completed; constructing a motion image recognition model, and performing motion area recognition on the disturbance response time sequence image sequence to obtain a time sequence motion area segmentation map; detecting internal and external impurity defects of the package according to the time sequence motion area segmentation map to obtain internal defect list data of the product; and when the product internal defect list data is non-empty, executing corresponding defective product removal control. High-precision intelligent identification of internal and external impurity defects of the liquid packaging product is realized through the image identification model, and the quality control level of a production line is remarkably improved.

The invention relates to the technical field of fire-fighting early warning, and discloses a fire-fighting early warning system based on image data relevance. The system comprises a multi-source image acquisition module used for acquiring fire-fighting scene multi-source heterogeneous image data; the correlation feature analysis module is used for performing cross-data-source correlation analysis on the data to generate feature vectors; the spatio-temporal dynamic modeling module is used for constructing a multi-dimensional feature fusion space to generate an associated spatio-temporal feature tensor; the resource optimization scheduling library is used for constructing a double-layer collaborative library; and the intelligent early warning control module generates a real-time fire early warning instruction and an emergency response decision through a hierarchical reinforcement learning framework. The system also has the functions of building structure deformation detection, smoke diffusion prediction and the like. Through cooperation of multiple modules, accurate fire-fighting early warning and efficient emergency response are realized, the fire prevention and control capability is improved, and life and property safety is effectively guaranteed.

The invention relates to an infrared and visible light image fusion method based on a cross-domain Transform, and belongs to the field of computer image processing. The method comprises the following steps: respectively carrying out preprocessing operation on an infrared image and a visible light image to obtain a training data set; an end-to-end image generator network is designed, an encoder module is used for extracting deep semantic features of an infrared image and a visible light image, a fusion module introduces an axial attention mechanism to enhance the global modeling capability of the features, and feature fusion is carried out in combination with information of a spatial domain and a frequency domain; the fused features are gradually recovered to an image space through a decoder module, and a fused image is generated; constructing a fusion loss function module, and guiding the network to focus a significant feature difference between the source image and the fusion image based on a comparative learning idea; and finally, inputting the infrared and visible light image Y channel into the network model, generating a fusion image, completing a training process, and realizing unified optimization of fusion performance and visual quality.

The invention discloses a TSV technology-based silicon wafer image defect identification method and system, and the method comprises the steps: synchronously collecting three-dimensional images according to the depth structure characteristics of a TSV silicon wafer, mapping the three-dimensional images of different modes to a unified space coordinate system, and obtaining a three-dimensional fusion image; according to the three-dimensional fusion image, a feature extraction model based on a three-dimensional convolutional neural network is adopted, TSV hole wall morphology, depth deviation and material uniformity features are extracted, and an enhanced multi-dimensional feature tensor is obtained; according to the multi-dimensional feature tensor, modeling in combination with the spatial topological relation of the TSV structure to obtain a spatial topological distribution diagram of the defects; according to the space topology distribution diagram of the defects, the relevance between defect causes and process links is analyzed, a defect cause report and process optimization suggestions are generated, and an interpretable defect diagnosis result is obtained. According to the embodiment of the invention, a comprehensive and accurate silicon wafer image defect identification and process optimization scheme can be provided, and the reliability of a TSV manufacturing process is improved.

The invention discloses a photovoltaic glass defect detection method and system based on image recognition, particularly relates to the technical field of visual inspection, and is used for solving the problem that defect recognition is interfered by optical artifacts caused by a film layer interference effect on the surface of coated glass. The method comprises the following steps: extracting interference characteristics through a reflection image to generate an optical artifact distribution diagram, positioning an interference region in a transmission image frequency domain space, constructing a process response template in combination with a sedimentary pulse frequency, and executing polarization coherence screening and time-varying deconvolution collaborative filtering to eliminate artifacts; based on energy ratio dynamic decision reinspection or defect classification process implementation, inherent optical noise and real physical defects of a material are effectively separated, and the accuracy and reliability of coated glass defect detection are remarkably improved.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

The invention relates to the technical field of drawing recognition, and discloses an engineering drawing intelligent recognition method and system based on deep learning. The method comprises the steps of performing multi-target cooperative detection on a first processing image based on a detection model, identifying primitive information of the first processing image, and generating a second identification image; positioning a text area of the second recognition image, recognizing a character detection range, determining word tags represented by the character detection range, summarizing character information of the character detection range based on the word tags, and generating third image data; obtaining a correlation degree among the symbols, the attributes and the connecting line information, detecting whether the primitive information accords with a preset rule or not, constructing a symbol topological relation graph, and generating correction information containing a conflict position; and fusing the primitive information, the character information and the correction information to generate structured data comprising a symbol hierarchy tree, an attribute incidence matrix and a conflict label. According to the invention, the efficiency and accuracy of engineering drawing intelligent identification are improved.

The invention discloses a data analysis problem generation method based on image input and large model combination, and the method comprises the following steps: S1, obtaining original image data, and carrying out the preprocessing of the original image data; s2, constructing a visual language model based on a Qwen-VL architecture, and performing bidirectional alignment to generate a visual feature vector; s3, constructing a cue word template, and fusing the cue word template through a cross attention mechanism to generate structured text description; s4, constructing a large language model based on a Transform architecture, and performing supervision and fine tuning by adopting a LoRA method to generate a data analysis problem candidate sequence; s5, semantic consistency detection and structural rule matching are carried out, and sequences which do not meet semantic specifications or structural constraints are removed; and S6, constructing an image-text alignment triple, and writing the image-text alignment triple into the data structure in the JSON format for coding and storage. According to the method, the image can be converted into a data analysis problem, and the text generation quality and efficiency are remarkably improved.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.