71 results about "Image fusion algorithm" patented technology

The invention discloses a robot automatic grabbing path planning method based on visual identification, and relates to the technical field of intelligent grabbing. The method comprises the following steps: acquiring multi-view visual data of a target scene, and generating a scene three-dimensional compact reconstruction model through a multi-modal image fusion algorithm; performing target detection and feature extraction on the model, and screening an optimal capture point in combination with a visual attention mechanism; constructing a dynamic environment obstacle probability map, updating an obstacle state through time sequence visual tracking, and quantifying an interference weight; an initial grabbing path is planned based on an improved fast expansion random tree algorithm, path smoothness constraints and robot joint movement limit parameters are introduced, and path nodes are optimized through a Bezier curve; visual servo feedback and path deviation prediction are fused, path parameters are corrected in real time, and a continuous movement track is generated. The method effectively adapts to the dynamic environment, gives consideration to path safety, smoothness and mechanical arm motion characteristics, and remarkably improves the grabbing success rate and operation reliability.

The invention discloses a rock and ore microscopic image splicing method and system based on deep learning, and relates to the field of image processing and the technical field of microscopes, and the method comprises the steps: obtaining a local rock and ore microscopic image of a rock and ore slice, carrying out the preprocessing of the local rock and ore microscopic image, and carrying out the overlapping region coarse registration of the preprocessed local rock and ore microscopic image through a phase correlation method; based on an improved image feature detection model, basic features and description features in the local rock and ore microscopic image after coarse registration are extracted, and local image features of the local rock and ore microscopic image are obtained; performing feature matching on the local image features of the two groups of local rock and mineral microscopic images by using an image feature matching model to obtain a matching corresponding relation of the local image features; and based on an image fusion algorithm of a homography matrix and a partial differential equation, splicing and optimizing the local rock and ore microscopic images in combination with a matching corresponding relation of local image features, and generating a large-view-field rock and ore microscopic image. According to the method, the complex transformation between the images can be better processed.

The invention provides a flue-cured tobacco leaf image distortion correction enhancement method, and belongs to the technical field of flue-cured tobaccos. Distortion segmentation point detection is carried out on an obtained flue-cured tobacco leaf image, and an illumination change area caused by unevenness of a tobacco leaf surface is identified by using an illumination homogenization algorithm based on a Poisson equation; secondly, constructing a distortion jump point matrix to mark gray abrupt change positions caused by shadows and light reflection between adjacent areas, then decomposing and reconstructing multi-scale image features by adopting a Laplacian pyramid image fusion algorithm, and eliminating optical interference generated by surface fluctuation through a designed fusion rule; and finishing processing when the accuracy reaches a set range, otherwise, carrying out correction and optimization again, thereby solving the technical problem that the image quality and the subsequent analysis accuracy are influenced by shadow and light reflection generated during image acquisition due to unevenness of the tobacco leaves in the prior art.

The invention is suitable for the field of intelligent jet cleaning, and provides a dirt recognition cleaning method, device and equipment, and the dirt recognition cleaning method comprises the steps: obtaining an original image corresponding to target dirt and a sound reflection signal corresponding to the target dirt; converting the sound reflection signal corresponding to the target dirt into an image representation; obtaining a fused image corresponding to the target dirt according to the original image corresponding to the target dirt, the image representation and a preset multi-channel image fusion algorithm; inputting a fusion image corresponding to the target dirt into the improved deep neural network to obtain segmentation mask data and size data of the target dirt; adjusting the pose of the dirt cleaning equipment according to the segmentation mask data and a preset coordinate conversion algorithm; according to the size data of the target dirt, dirt cleaning parameters are adjusted, and dirt cleaning equipment is controlled to execute cleaning operation. According to the method, the dirt recognition and cleaning precision and efficiency are remarkably improved, and various cleaning requirements and operation environments can be better met.

The invention provides a power transmission line bird damage risk assessment and early warning method and system, and relates to the technical field of intelligent power grid environment perception and risk early warning, and the method comprises the steps: data collection and multi-modal fusion: collecting image data, voiceprint data, meteorological parameters and ecological environment information along a power transmission line to form multi-modal data, the multi-modal data is uniformly coded by synchronizing timestamps and geographic coordinates, an original bird activity sample set is generated, and for the problems that the monitoring means is single and the recognition precision is insufficient, image, voiceprint, weather and ecological information are fused through a multi-modal data acquisition module, so that the recognition precision is improved. The acousto-optic image fusion algorithm and the two-channel convolutional neural network are adopted, all-weather bird type and behavior accurate recognition is achieved, the recognition accuracy is improved, key behaviors such as nesting and dung pulling can be stably captured even at night or in a foggy environment, and potential risk missing detection is remarkably reduced.

The invention relates to the technical field of image fusion, and provides an infrared and visible light image fusion algorithm based on a hybrid expert system, and the infrared and visible light image fusion algorithm comprises the following steps: S1, carrying out the cross feature extraction of an input infrared image and an input visible light image; s2, inputting a cross feature extraction result into a gating module composed of a full connection layer; S3, selecting two environment categories with the maximum probability value, and executing differential fusion processing in an expert model; S31, splitting an infrared image and a visible light image into local windows, then through a wavelet adaptive transformation module, performing multi-band decomposition on each window and applying an extra weight coefficient to generate a preliminary fusion image; s32, performing edge feature extraction, spot feature extraction and feature fusion processing on the preliminary fusion image to obtain a final fusion image; and S4, performing weighted fusion on the final fusion image through the trainable weight coefficient to obtain a final output image.

The invention provides a soil humidity prediction method and device, electronic equipment and a storage medium, and relates to the technical field of soil humidity prediction.The method comprises the steps that current meteorological data and predicted meteorological data are obtained, the predicted meteorological data comprise meteorological data of N days in the future, and N is larger than or equal to 1; acquiring Landsat remote sensing image data and MODIS remote sensing image data; based on a GEE image fusion algorithm, estimating the Landsat remote sensing image data through the MODIS remote sensing image data to obtain fused remote sensing image data; fusing remote sensing image data to obtain a fused spectral index; and inputting the current meteorological data, the predicted meteorological data and the fused spectral index into a hierarchical soil humidity model to obtain a predicted soil humidity data set. According to the invention, the soil humidity prediction precision is improved.

The embodiment of the invention provides a dike abnormal target object detection method based on infrared and visible light image fusion, which is used for identifying a dike target object needing to be detected in a dike disaster scene, and comprises the following steps: obtaining an infrared image and a visible light image; registering the infrared image and the visible light image by adopting a feature matching algorithm; fusing the registered infrared image and visible light image by adopting an image fusion algorithm to obtain a fused image; and iteratively executing the following segmentation optimization operations until the calculated value of the target function of the mean shift algorithm is minimum to obtain the image segmentation result of the fused image: segmenting the fused image by adopting the mean shift algorithm to obtain the current segmentation result, determining the calculated value of the target function according to the current segmentation result, and obtaining the image segmentation result of the fused image. And optimizing hyper-parameters of the mean shift algorithm according to the calculated value of the target function. According to the scheme, the embankment target object with a small size can be accurately segmented, and the problem of excessive segmentation can be solved.

The invention discloses an intelligent inspection method and system based on night vision digital glasses. According to the method, a thermal infrared image, a visible light image, depth information and ambient light intensity data of an inspection area are collected in real time through night vision digital glasses. Feature level fusion is carried out on the infrared image and the visible light image by using an image fusion algorithm, depth information is corrected and optimized, and imaging parameters are automatically adjusted according to ambient light intensity data to ensure that the image is clear. Then, a deep learning algorithm is used to carry out real-time analysis on fused image data, a target is identified, and three-dimensional positioning and distance measurement are realized. And in combination with a preset risk model and a decision rule, the risk is evaluated, an acousto-optic early warning is sent to the inspection personnel through the glasses display module when an abnormality is found, and the risk information is transmitted to a remote monitoring center. In addition, inspection data are automatically recorded and an inspection report is generated. The method and the system effectively improve the inspection efficiency and accuracy, and are especially suitable for intelligent inspection tasks in complex environments.

The invention discloses a field operation portable low-light puncture infusion guide system which comprises a multispectral imaging module used for collecting an image containing a plurality of spectral bands by utilizing the penetrating power and reflection characteristic difference of light rays with different wavelengths to human tissues; the near-infrared blood vessel sensor is used for acquiring the contour and the position of a blood vessel; the low-light sensor is used for converting the optical signal into an electric signal based on a photomultiplier or a CMOS (Complementary Metal Oxide Semiconductor) low-illumination imaging technology and generating a low-light image through amplification and noise reduction processing; the image fusion processor is used for carrying out image fusion on the blood vessel image and the low-light-level image based on a feature-level or pixel-level image fusion algorithm; the AR display module is used for superposing a virtual image which is generated by image fusion and contains the position and the direction of the blood vessel into a real scene; the method has the advantages that the multispectral imaging technology and the low-light sensing technology are combined, richer tissue information is obtained, the limitation of a single imaging mode in the low-light environment is solved, and the blood vessel visualization effect is improved.

The application discloses an infrared and visible light image fusion algorithm based on a self-encoder, and comprises the following steps: S1, an encoder network is constructed by combining a short cross-layer connection with an ECA attention mechanism; S2, registered infrared and visible light images are input into the encoder network, and the encoder and decoder network are trained to obtain an encoder meeting a condition; S3, feature fusion is performed on the input images by using the constructed fusion network, and the fusion network is trained to obtain a fusion network meeting a condition; and S4, the fused images are decoded and reconstructed by using the decoder.The evaluation index of the fused images is significantly improved, the target is clear, the details are prominent, the outline is obvious, and the images meet the human visual perception.

This invention discloses a visual inspection method and apparatus for glass production quality, relating to the field of glass quality inspection technology. The method includes the following steps: constructing a polarization excitation detection field; acquiring and registering a sequence of original polarization images of the glass based on the polarization excitation detection field to obtain a polarization image sequence; performing image fusion on the polarization image sequence using an image fusion algorithm based on non-subsampled contour wave transform, sparse representation, and guided filters to obtain a defect-enhanced image; inputting the defect-enhanced image into a lightweight glass defect recognition model based on improved self-attention, outputting a glass defect category, acquiring glass defect quality impact data, and classifying glass quality grades based on the glass defect category and the glass defect quality impact data, thereby achieving visual inspection of glass production quality.

The invention provides an infrared and visible light image fusion method based on a second-order attention mixed feature, and the method comprises the steps: inputting the first-order and second-order statistics of an infrared image and a visible light image, and adaptively integrating the global features and local features of the infrared and visible light images, thereby enabling the fused image to obtain a stronger feature expression, and improving the image fusion precision. Pixel intensity and texture details are reserved at the same time, comparable contrast is kept while a more vivid edge is achieved, an output result keeps rich edge and texture information transmitted by an input image, artifacts are few, and visual perception of human beings is better met. Compared with other infrared and visible light image fusion algorithms based on deep learning, the method has a better fusion effect.

The invention relates to the technical field of image acquisition, in particular to a device and a method for acquiring a reflection-removing image on the surface of a high-reflection injection molded part, and the acquisition device comprises a multi-camera assembly, a light source adjusting assembly, a workpiece moving assembly and a control and processing module; the method comprises the steps that the distance between a multi-camera assembly and a workpiece, the distance between a light source adjusting assembly and the workpiece and the angle of a strip-shaped light source in the light source adjusting assembly are adjusted, the situation that strong reflected light is directly received by cameras is reduced, and a workpiece moving assembly is started to drive the workpiece to move; in the moving process of the workpiece, the multi-camera assembly synchronously collects injection molding part surface images at different visual angles, and an image sequence containing complementary information is obtained; according to the invention, a multi-view complementary image is provided for an image fusion algorithm through the multi-camera assembly and the workpiece motion assembly through a strategy of combining a mechanical structure design and a software algorithm, more image details are reserved while a reflective region in the image is removed, and the final image quality is improved.

The application is a kind of vehicle-in-the-loop test method based on visual augmented reality. First, the positioning and motion information acquisition system of the real vehicle is established, and the high-precision motion information of the vehicle can be obtained. Then, the image information and motion information of the vehicle-mounted camera are synchronously collected, and the purpose is to obtain the camera output and vehicle motion information under the same time stamp. Secondly, through the internal parameters and initial external parameter information of the camera, the pose of the camera at each moment is calculated by combining the synchronously collected motion information. The calculated pose is synchronously input into the simulation software to obtain the virtual camera output consistent with the real camera in time and space. The real camera image collected is taken as the background, and the traffic participants provided by the virtual simulation camera are taken as the foreground. Through the image fusion algorithm, the fusion image information of the augmented reality perspective is obtained. Finally, the fusion image is input to the perception layer, the perception result is calculated and input to the decision control layer, and the decision instruction is output to the execution mechanism of the vehicle, so that the vehicle motion information is changed and collected by the high-precision information acquisition system, thereby completing a cycle of information.

The invention relates to a photovoltaic module fault detection method fusing infrared and visible light in the technical field of multi-modal image intelligent monitoring, and the method comprises the steps: obtaining dual-modal image data through employing a visible light and infrared camera carried by an unmanned plane, carrying out the preprocessing and arrangement of the dual-modal image data into a YOLO training format data set, carrying out the enhancement processing through an image fusion algorithm, and carrying out the detection of a fault of a photovoltaic module. And training a target detection model based on a YOLO11 framework to judge a fault type recognition effect, exporting a final weight file, compiling and optimizing an ONNX format model by using a TensorRT tool, generating an inference engine file, deploying the ONNX format model to a development board after optimization, constructing a visual interface, and realizing real-time shooting detection and visual display of the unmanned aerial vehicle. According to the method, the target identification precision is improved, reliable technical support is provided for actual deployment, a complete solution from multi-mode perception and feature fusion to efficient deployment is provided, and the method has important value for improving the inspection efficiency and the operation intelligence level of the photovoltaic power station.

This invention relates to the field of inkjet characteristic measurement technology, specifically to inkjet characteristic measurement methods, systems, equipment, and storage media. A host industrial control computer ensures efficient inkjet start-up control, while a printhead driver board ensures stable, accurate, and timely inkjet printing. A camera device captures images via commands for subsequent analysis. Iterative operations combined with image fusion algorithms enable rapid, continuous, and accurate measurement of arrayed printheads, aiding in feature recognition. Image recognition methods derive droplet characteristics for precise analysis, and repeated motion stages comprehensively acquire characteristics to generate inkjet characteristics, strongly supporting inkjet system optimization and improving product quality and production efficiency.

This invention discloses a fully automated microscopic scanning and result judgment system for TAP detection. This invention relates to the field of biomedical engineering technology within the bio-industry. The central control and processing unit runs TAP feature analysis logic, which is configured to: identify suspected TAP agglomerates under low magnification using color threshold segmentation; perform multi-focal plane Z-axis tomography on the suspected TAP agglomerate regions under high magnification; generate a clear panoramic image of the TAP agglomerates using a multi-focal plane image fusion algorithm; and extract the morphological and textural features of the agglomerates from the fused image. This invention addresses the three-dimensional stacking characteristics of TAP agglomerates, unlike existing technologies that treat them as defects and discard them. Instead, it creatively introduces Z-axis tomography and multi-focal plane fusion technology, transforming "blurriness" into "features," thus fully acquiring the spatial morphological information of the TAP agglomerates and significantly improving detection sensitivity.

The invention discloses a dynamic multi-frame image fusion algorithm and system. The method comprises the following steps: S1) inputting N frames of images; s2) realizing accurate alignment of multi-time sequence data by estimating and compensating random drift caused by Brownian motion; s3) performing total rigidity compensation; s4) carrying out sub-pixel-level image registration, and carrying out elastic deformation compensation by constructing a local deformation field of B-spline primary function interpolation and carrying out optimization solution; s5), constructing a self-adaptive weight superposition model, and obtaining high-resolution output; the system comprises a preprocessing module, an iterative optimization module and an output module which are connected in sequence. According to the dynamic multi-frame image fusion algorithm and system, the noise perception dynamic weight model is used, the weight is adaptively adjusted in the space-time domain, and the signal-to-noise ratio is remarkably improved and is superior to that of traditional average superposition; meanwhile, the Brownian motion physical model is embedded into an image registration process by utilizing a physical-data dual-drive framework, so that the precision of a pure data drive method is improved.

The invention discloses an optical lens and a preparation method and application thereof. The optical lens comprises a transparent substrate, a micro-lens array located on the first surface and a super-lens array located on the second surface. The transparent substrate comprises a first surface and a second surface which are oppositely arranged. The microlens array comprises a plurality of microlenses arranged at intervals, each microlens forms a sub-view field, and the plurality of sub-view fields jointly form a total view field. The super lens array comprises super lenses in one-to-one correspondence with the micro lenses, and the super lenses compensate the wavefront difference of the micro lenses through phase distribution. The large field of view is decomposed into a plurality of small sub-fields of view through the micro-lens array, gradient phase distribution is realized through the nano-structure of the super-lens, the residual wavefront difference of the micro-lens is compensated, the aberration generated by the micro-lens is accurately corrected, and after light rays finally reach the imaging sensor array, large FOV seamless imaging can be realized in combination with an image fusion algorithm.

The application provides an infrared and visible light image fusion method and system based on bidirectional semantic-space alignment and differential perception, and relates to the technical field of image processing. In order to solve the problem that the expression capacity is insufficient when the existing method processes complex cross-modal semantic interaction. The technical points of the application include: S1, collecting infrared images and visible light images; S2, fusing infrared and visible light images based on a semantic fusion network, wherein the semantic fusion network is based on a PSFusion network model, the SDFM module of the PSFusion network model is replaced by a differential perception complementary module DACM, the PSFM module is replaced by a deep semantic fusion block DSFB, and a space semantic alignment module SSAM is added after the deep semantic fusion block and the differential perception complementary module; S3, image reconstruction and supervision are performed on the aligned features. The method provided by the application shows superior potential compared with the current advanced image fusion algorithm.

The application relates to the technical field of oral medicine, and discloses an orthodontic micro-implant AI-MR navigation and mark clamp registration system, which comprises the following modules: a data fusion module, which collects CBCT data and intraoral scanning data, and uses an image fusion algorithm to construct a three-dimensional digital dental model; an intelligent path planning module, which analyzes tooth root and alveolar bone morphology by using an AI algorithm, and plans an optimal implantation path; a registration and virtual-real registration module, which realizes virtual-real registration by clamping a registration mark clamp or other registration anchor point in the incisal edge area of anterior teeth, constructs an MR space registration post-model, and maps the optimal implantation path to a real coordinate system to obtain a virtual path; and an intraoperative navigation module, which tracks real-time position changes of an MR head-mounted device and updates a virtual path superposition position, and performs navigation guidance in the whole implantation process. The application scheme can improve positioning accuracy, operation visibility and system adaptability in the implantation process of an orthodontic micro-implant.

The invention relates to a remote monitoring and fault diagnosis method and system for 10kV switch cabinets. The remote monitoring and fault diagnosis method comprises the following steps: S1, synchronously acquiring infrared thermal images, visible light images and electrical parameters and environmental parameters related to equipment operation states in a plurality of switch cabinets; s2, converging data from different switch cabinets, performing protocol conversion, preprocessing and timestamp alignment on the converged data, and extracting key feature data from the converged data to form a structured data packet; and S3, based on the structured data packet, generating an equipment health state assessment result and hierarchical early warning information by using a multi-channel image fusion algorithm and a multi-level early warning model of a state stability index. The method has the advantages that early warning can be given out at the initial stage of equipment abnormity, so that fault processing is changed from post-maintenance to pre-prevention, and the equipment fault rate is reduced; the unmanned automatic inspection is realized, the manual inspection frequency is reduced, the labor cost is reduced, and the monitoring precision and the diagnosis reliability are greatly improved.

The invention provides a multispectral detection method and system based on a quantum dot coating CMOS sensor and a medium, and the method comprises the steps: collecting a multispectral original image, and carrying out the spectral normalization processing of the multispectral original image, and obtaining a normalized image; carrying out pixel geometric registration on the normalized image to obtain a registered image; extracting spectral features based on the registered image, performing principal component and waveband analysis on the spectral features, and performing fusion processing on the registered image based on an analysis result to obtain a fused image; constructing a material identification and pollution analysis model, inputting the fused image into the material identification and pollution analysis model, outputting a detection result, and transmitting the detection result to a terminal in real time; according to the method, reflection and absorption image information of ultraviolet-visible-infrared broadband can be obtained through single exposure, component and morphology information of the material is obtained through an image fusion algorithm, and material residues, metal diffusion, pollutants and the like are recognized and quantitatively analyzed.

This invention discloses a multimodal medical image fusion method based on a global optimization model, comprising: acquiring registered image pairs: anatomical medical images as grayscale images and functional images as three-channel color images; inputting the extracted illumination component of the functional image and the single-channel grayscale image into the image module of global optimization decomposition respectively to obtain their corresponding base layer and detail layer; fusing the obtained base layer and detail layer respectively; linearly adding the obtained fused base layer and fused detail layer to obtain the fused illumination component; and merging the new illumination component with the color component of the input functional image to obtain the final fused image. This invention effectively solves the uncertainty of parameter selection in medical image fusion algorithms and directly obtains the ideal fused image through an optimization function method.

The invention relates to a prostate puncture robot which improves the accuracy and safety of a puncture operation through the fusion of mechanical structure innovation and an intelligent algorithm. The system comprises mechanical structures such as an end effector device and a positioning device, and intelligent control modules such as a multi-modal medical image fusion algorithm and a self-adaptive force control algorithm. Through multi-modal data fusion, reinforcement learning path planning and real-time motion compensation, precise planning and dynamic adjustment of a puncture path are realized, the puncture accuracy and safety are remarkably improved, and the method is suitable for diagnosis and treatment of prostatic cancer.

The invention relates to a visual identification system and method for goods sorting and transportation, and relates to the technical field of logistics transportation control, and the visual identification system comprises an image collection module which collects original goods images of goods in transportation according to different visual angles; the image processing module is used for cleaning and standardizing the original article image to obtain an article multi-view linked image and extracting a logistics distribution position; the model construction module is used for aggregating historical article images into a training set and generating a multi-view sorting and distinguishing model by combining with a target detection algorithm for iterative training; the screening identification module is used for judging an article multi-view linked graph according to a multi-view sorting judgment model, and determining an article sorting conclusion in combination with a logistics distribution position; the updating optimization module is used for filling an article multi-view linked graph into an article database according to an article sorting conclusion and optimizing a multi-view sorting judgment model; a sorting discrimination model is constructed through a multi-view image fusion algorithm and a deep convolutional neural network, and the precision and efficiency of intelligent logistics sorting are improved.

The invention provides an array camera parameter splicing method and device and a medium, and can solve the problem that the performance of an array camera is difficult to quantitatively evaluate after calibration in the prior art. The method comprises the following steps: for an image collected by each sub-camera in an array camera, dividing the image into a plurality of image blocks, and extracting a pixel coordinate of an angular point of each image block; projecting the image block angular points to a laser radar coordinate system by using the extrinsic parameter matrix corresponding to the sub-camera to which each image block angular point belongs, and calculating a cylindrical surface intersection point coordinate of each image block angular point and the target cylindrical surface in the laser radar coordinate system; based on the cylindrical surface intersection point coordinates of the image block angular points of all the sub-cameras, performing image drawing under a laser radar coordinate system to obtain a primary spliced image; processing an overlapping region in the primary spliced image through an image fusion algorithm, and generating a seamless spliced image of the target cylindrical surface; and calculating spatial geometric parameters of the array camera based on the seamless spliced image.

The application belongs to the technical field of computer vision, and discloses a remote sensing target detection method, system, medium, equipment and terminal, constructs an RGB-infrared image fusion network based on CNN, adopts a task-oriented fusion method, a low-layer and high-layer joint adaptive training strategy, and utilizes a joint loss composed of content loss and semantic loss to constrain the fusion network and the fusion method of salient target detection; angle information is introduced to predict the angle by using Gaussian cycle encoding and rectangular cycle encoding, feature extraction is carried out, and a BIFPN bidirectional feature fusion method is used for feature fusion; a real-time collected multi-modal video stream is used for decoding, algorithm reasoning and boundary box calibration, and the result is encoded and pushed to a streaming media server or a client. The application can quickly and accurately detect the vehicle position and category information under strong light and weak light conditions by combining the image fusion algorithm and the rotating target detection algorithm, and is applied to the rotating target detection in the unmanned aerial vehicle traffic management and disaster rescue scene.