152 results about "Laplacian pyramid" patented technology

The invention discloses an intelligent steel pipe surface defect recognition system based on deep learning, and particularly relates to the technical field of pipe surface defect analysis. An annular polarization light source array and a high-frame-rate CMOS sensor are adopted to synchronously collect visible light and near-infrared multi-polarization images; a surface normal is calculated based on Stokes parameters, mirror surface suppression and diffuse reflection enhancement are realized, a defect candidate area is generated by fusing multi-scale Laplacian pyramid residual error and Renyi entropy segmentation threshold positioning, multi-physical quantity registration is completed through white light interference and infrared thermal imaging, a six-channel feature cube is constructed, and a three-dimensional image is obtained. According to the method, space, spectrum and thermal characteristics are jointly extracted in the multi-head attention convolutional neural network, the confidence coefficient is evaluated in combination with Jensen-Shannon divergence, and the polarization angle and the focal length are dynamically adjusted according to the confidence coefficient, so that closed-loop parameter self-optimization is realized, and the micro-scale pitting corrosion and millimeter-scale crack detection precision is remarkably improved.

The invention relates to an underwater image processing technology in the field of underwater autonomous perception, in particular to an underwater low-quality image enhancement method based on a Laplacian pyramid and contrast learning. Comprising the following steps: S1, image input and multi-scale decomposition: decomposing an input image into a low-frequency residual layer and a plurality of high-frequency detail layers through Laplacian pyramid decomposition; s2, inputting the low-frequency residual error layer into a global illumination and color correction sub-module to obtain an enhanced low-frequency residual error layer; s3, inputting the enhanced low-frequency residual error layer and the multi-scale high-frequency detail layer into a frequency domain enhancement feature module to obtain multi-scale enhanced high-frequency detail layer output; s4, performing progressive reconstruction on the enhanced low-frequency layer output and the high-frequency enhancement layer output of each scale according to the inverse process of the Laplacian pyramid; and S5, outputting a result and applying. The method can be used for sharpening optical image data in the operation task of the autonomous underwater vehicle, and the image quality is improved.

The invention discloses a large-breadth cross-scale adhesive film defect detection method and a large-breadth cross-scale adhesive film defect detection device. The method comprises the following steps: carrying out coarse focusing on a camera and capturing a glue film image, carrying out coarse scanning on the glue film image and extracting information of a printed image-text in the glue film image by using an image difference method, inputting an output difference image into an anchor frame algorithm, inputting an output fine scanning target area coordinate into a genetic algorithm, and carrying out fine scanning on the glue film image. Outputting an optimized fine scanning path in combination with S curve planning characteristics of the motor; the motion platform is driven to complete path execution by combining double-axis linkage control with the fine scanning path, camera focusing is completed in the path execution process, and a high-resolution image of a defect area is collected and registered; and carrying out image splicing and fusion on the registered images by combining an optimal suture line algorithm with an improved Laplacian pyramid fusion method, and carrying out defect detection on the fused complete adhesive film image. The method has the advantages of high splicing precision and fast processing efficiency, and can effectively retain more detail information.

The invention relates to the technical field of computer image processing, and particularly discloses a double-branch defogging method based on a Laplacian pyramid, and the method comprises the following steps: S1, building a training data set A containing an original clear image and a corresponding foggy image; s2, performing cutting and data enhancement processing on the input image; s3, a defogging network LPSDNet based on the Laplacian pyramid is constructed, the defogging network LPSDNet comprises a multi-feature fusion focusing branch and a Laplacian pyramid supplementary branch, and after feature extraction of the two branches is completed, features learned by the two branches are fused through a learnable fusion module; s4, training the defogging network LPSDNet by adopting the training data set A until a preset loss function is converged; obtaining a trained defogging network LPSDNet in combination with an objective function; and S5, inputting an image to be defogged into the trained defogging network LPSDNet for testing and verification to obtain a defogging result. The global structure information of the image can be fully captured, and the distortion phenomenon is prevented.

The present invention discloses a rapid construction method and system for a wireless channel knowledge map. The present invention uses image restoration technology to construct a channel knowledge map, and, by means of recovering a corresponding pixel value in an image matrix, predicts channel knowledge of a specific position, so that channel related knowledge of a user equipment at any position in a target area can be acquired. By means of combining the similarity of an image restoration problem and a channel knowledge map construction problem, and using a Laplacian pyramid frequency band decomposition framework, an input environment map can be decomposed at different frequencies. According to the characteristics of different frequency components, a corresponding sub-network is designed for feature extraction, and finally, the channel knowledge map is reconstructed by means of inverse operation of the Laplacian pyramid. The wireless channel knowledge map construction method provided by the present invention not only implements higher construction precision at lower computational complexity, but also has strong generalization ability for various wireless communication scenarios.

The invention discloses a steel-wood combined beam-column joint damage identification method and system applying machine vision, and the method comprises the steps: S1, obtaining a visible light, near-infrared and thermal imaging multi-mode image sequence of a steel-wood combined beam-column joint, and carrying out the denoising processing through adaptive anisotropic diffusion filtering, and obtaining a denoised multi-mode image sequence; s2, performing Gaussian pyramid decomposition and Laplacian pyramid feature fusion on the de-noised multi-modal image sequence tensor to obtain a fusion feature map; s3, performing adaptive threshold segmentation and morphological processing on the fused feature map to obtain a final binary image; s4, training the deep learning network to obtain a trained deep learning network; and S5, performing damage identification by using the trained deep learning network. According to the method, the steel-wood combined beam column joint damage can be accurately identified, the segmentation probability graph is obtained, the segmentation result is marked, and the visualization of the damage area is realized.

The invention relates to the technical field of underwater sediment measurement, in particular to a microspur measurement device and method, and provides the following scheme: firstly, screening out an initial local selected area through overall image graying and feature extraction, and adjusting the local area through iteration to ensure that a microspur image can accurately reflect particle features in the overall image; then, decomposing and extracting high-frequency edge information by using a Laplacian pyramid, eliminating background noise through gray variance, and generating a particle region graph; further separating adjacent particles by using an adhesive particle cutting technology, and finally generating a high-quality sediment image through image correction; according to the method, the microspur image acquisition frequency can be reduced, the particle measurement accuracy is improved, the data processing complexity is reduced, and the method has relatively high measurement precision and practical value.

The invention relates to the technical field of underwater image enhancement processing, in particular to a pyramid network-based underwater image dynamic enhancement method and application, and the method comprises the steps: decomposing an input underwater image into layered image groups; using a deterministic color mapping network to convert the low-frequency layer input image into a low-frequency layer output image; using a double convolution attention module to obtain enhanced input features; using a packet expansion feedforward network to obtain a pixel-by-pixel mask; performing operation on the enhanced input features and step-by-step pixel masks to obtain high-frequency components; and reconstructing the low-frequency layer output image and the high-frequency component by using a Laplacian pyramid network to obtain an enhanced output underwater image. In conclusion, the underwater image can be dynamically enhanced, the adaptability is high, the method is suitable for various water bodies, color distortion can be effectively corrected, noise introduction is restrained, more image details can be reserved, and the underwater image has better visibility and higher recognition degree.

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 discloses a low-light image enhancement method based on multilevel feature fusion. The low-light image enhancement method comprises the steps of acquiring a data set, dividing the data set, extracting features, constructing a synchronous multi-scale network, training the synchronous multi-scale network and testing the synchronous multi-scale network. According to the synchronous multi-scale low-light image enhancement method in combination with the Laplacian pyramid, the input image is processed in parallel by adopting a double-path structure: the preliminary enhancement image is obtained through the local-global convolutional neural network, and the detail and texture information of the image is enhanced based on the Laplacian pyramid decomposition network. A multi-scale network is adopted to process images in scenes of deblurring, defogging, rain removal, low light enhancement and the like, and details and features are extracted in a layered manner, so that the definition, color and contrast ratio of the images are effectively improved. Comparison experiments prove that the method has the advantages that noise is effectively suppressed, and remarkable effects are achieved in the aspects of detail recovery and color restoration. The method is suitable for image enhancement processing under various complex illumination conditions.

The invention discloses a medical image segmentation method based on a Laplacian pyramid and a dynamic Transform. The method comprises the following steps: S1, forming a preprocessed medical image; s2, decomposing the preprocessed medical image into a plurality of image sub-layers with different scales; s3, generating a corresponding multi-scale feature representation; s4, inputting the multi-scale feature representation into a dynamic Transform module, and generating a fused global feature representation; and S5, decoding the fused global feature representation to generate a preliminary segmentation image, applying a post-processing algorithm to the preliminary segmentation image, and executing morphological operation, edge repair and noise removal to obtain a medical image segmentation result with clear boundary and regional coherence. According to the method, a medical image segmentation result with clear boundary, coherent region and accurate semantics is generated, the practicability of the model in clinical application is structurally improved, and the method has significant advantages in processing images with fuzzy tumor boundary and dense and overlapped tissues.

The invention discloses an equipment micro-vibration real-time monitoring method and system based on visual enhancement, and belongs to the field of equipment micro-vibration monitoring, and the method comprises the following steps: S1, collecting a high-frame-rate video, and recording collection parameters in real time; s2, constructing a Gaussian pyramid, constructing a Laplacian pyramid through adjacent layer image difference of the Gaussian pyramid, and decomposing the collected video into different spatial frequency bands; s3, designing a band-pass filter to extract a specific frequency band where the equipment vibration signal is located, and enhancing and reconstructing the micro vibration signal; s4, carrying out region selection from the enhanced image frame, calculating an optical flow in a selected region through an optical flow method, and extracting a characteristic frequency of vibration; and S5, performing data visualization, and generating a vibration signal image in real time to display the motion characteristics of the target area. According to the invention, the problems of insufficient sensitivity and poor real-time performance of small vibration detection of equipment in a traditional vibration monitoring method can be solved.

The invention relates to the technical field of semantic segmentation in computer vision, and discloses an urban scene-oriented semantic segmentation method for unmanned aerial vehicle aerial images, which comprises the following steps of: collecting a large amount of unmanned aerial vehicle aerial image data to form a semantic segmentation label set; taking the semantic segmentation tag set as the input of a feature extraction module, and training the feature extraction module; taking the semantic segmentation tag set as the input of a Laplacian pyramid module, and training the Laplacian pyramid module; taking the feature map output by the feature extraction module and the feature component output by the Laplacian pyramid module as the input of a feature fusion module, and training the feature fusion module; inputting the semantic segmentation tag set into a detail tag processing module to obtain a semantic segmentation edge detail tag graph; and calculating the loss of the semantic segmentation model. According to the method, the semantic segmentation problem of the aerial image of the unmanned aerial vehicle in the urban scene is solved, so that the safety and practicability of the unmanned aerial vehicle in the urban environment are improved.

The invention discloses a low-defect-stress SiC crystal and a SiC substrate, and belongs to the technical field of SiC material preparation. A plurality of SiC substrates are prepared from the SiC crystal, high-pass signals and low-pass signals are distinguished on the basis of the Laplacian pyramid principle, and under the high-pass signals, the overall optical path difference of any SiC substrate is lt; 6 nm, the maximum value of the optical path difference is lt; the standard deviation of the optical path difference is lt; and 3nm. The optical path difference between the SiC crystal and the SiC substrate is small and uniform under a high pass signal, the number of defects in SiC is small, stress concentration caused by in-plane defects is small, and the crystal quality of SiC is more uniform.

The invention relates to the technical field of image segmentation, in particular to an anaphora image segmentation method based on space-frequency dual tuning, and the method comprises the steps: dynamically generating a frequency domain learning prompt and a space domain learning prompt through a space frequency domain dual prompt technology; splicing interaction is carried out by utilizing spatial domain prompts and encoder features, and semantic representation of an anaphora target is enhanced; recovering high-frequency details by adopting a Laplacian pyramid and a LoRA-based frequency domain encoder, and enhancing key features through multiplication operation prompted by a frequency domain; and finally, double-domain optimization features are fused, and an accurate boundary segmentation result is generated through a mask decoder, so that the problems of detail degradation and boundary fuzziness caused by lack of frequency domain information in a traditional method are effectively solved.

The invention provides a low-light image anti-overexposure enhancement method based on multiple scales and an attention mechanism. The problem of overexposure after image enhancement in the existing method is solved. According to the method, the Laplacian pyramid of the image is utilized, the deep neural network is used for extracting multi-scale information of the low-light image, and detail features of the image are effectively mined and reserved. And adjusting and fusing the multi-scale information by adopting a targeted processing strategy, and recovering to obtain a fine illumination image capable of accurately representing external illumination distribution. And a targeted attention mechanism is added during processing of each sub-network for adjustment so as to realize image enhancement. According to the method, the Laplacian pyramid information of the image is fully utilized, and the effect of enhancing the dark light image is good.

The invention discloses a non-intrusive load identification method based on load high-frequency feature imaging, and aims to realize comprehensive optimization of economical efficiency, low-carbon property and stability of a power system. At a power supply inlet end, a high-precision constantan resistor and a high-speed data acquisition card are used for acquiring current and voltage data, frequency domain information is acquired through Fourier transform, and related parameters are calculated to complete feature extraction. Converting the frequency domain features into a two-dimensional image through discrete cosine transform, and synthesizing current and power feature images into an RGB image by using a Laplacian pyramid fusion technology; and finally, selecting a lightweight deep learning model OfficientNet, training by using a fused image, and adjusting weight deviation to identify load characteristics. The method overcomes the defects of the traditional technology in the aspect of load identification, facilitates reasonable distribution of electric power resources, reduction of energy consumption, improvement of energy utilization efficiency and guarantee of stable and reliable operation of an electric power system through accurate identification of the load, and has important application value in the field of electric power system management.

The invention discloses a multi-scene statistical channel fingerprint construction method and system based on frequency perception. According to the method, unified tensor modeling is carried out on statistical channel state information, and various typical statistical channel fingerprint construction scenes are uniformly expressed as a tensor recovery problem. On the basis, multi-scale frequency decomposition and reconstruction of the statistical channel fingerprints are realized through a Laplacian pyramid, and multi-scale frequency sub-band features of the statistical channel fingerprints are obtained while the reasoning efficiency is improved; and through a shared mask learning mechanism, carrying out layer-by-layer self-adaptive refinement processing on high-frequency statistical channel fingerprint components under different scales. Besides, in order to expand the characteristic receptive field under the condition that the model parameter quantity is not obviously increased, convolution processing is carried out on different frequency components in a wavelet transform domain, and decoupling extraction and efficient modeling of the statistical channel fingerprint multi-frequency characteristics are achieved. According to the method, the construction precision and the calculation efficiency can be considered in various statistical channel fingerprint construction scenes.

The invention relates to an image recognition method and system for a pointer type meter. The method comprises the following steps: acquiring an original image of a meter to be recognized; fusing the continuous multiple frames of original images through a Laplacian pyramid method to obtain a fused image; performing edge detection on the fused image through a Prewitt operator to obtain an edge enhanced image; establishing double detection heads, and respectively detecting the positions of a pointer and a dial in the edge enhancement image through the double detection heads to obtain position information; and according to the position information, calculating a relative angle between the pointer and the dial through a Hough transform method, and converting the relative angle into a reading so as to complete image recognition of the pointer type meter. According to the method, the accuracy and robustness of pointer meter image recognition in the weak light environment are effectively improved.

The invention belongs to the technical field of image analysis and processing, and relates to an automobile circuit board solder paste printing quality detection method and system. The method comprises the steps of constructing a Laplacian pyramid for a target solder paste area image, extracting multi-level geometric and position features, performing weighting based on local phase consistency to obtain a first fusion feature, and comparing the first fusion feature with a first fusion feature of a historical qualified sample to obtain a second fusion feature; calculating a time sequence drift correction characteristic capable of correcting the drift in the production process; constructing a neighborhood feature map by combining the first fusion features of the target region and the spatial neighborhood of the target region, and extracting spatial morphological features by using an orthogonal Gabor filter; and performing vector splicing on the first fusion feature, the time sequence drift correction feature and the spatial form feature to generate a comprehensive combined feature, and inputting the comprehensive combined feature into a predetermined classification model to obtain a printing quality detection result. According to the invention, the detection rate of the solder paste area defect on the automobile circuit board can be improved.

The invention belongs to the technical field of image enhancement, and particularly discloses a low-illumination image enhancement method based on an information fusion strategy, and the method comprises the steps: carrying out the processing of an inputted low-illumination image through an image enhancement algorithm based on adaptive Gamma correction, and generating a first group of multi-exposure image sequences; processing the same input image through an improved quadratic function enhancement algorithm to generate a second group of multi-exposure image sequences; and performing multi-scale fusion on the first group of multi-exposure image sequences and the second group of multi-exposure image sequences, including distributing the weight of each image through a weight strategy, performing multi-scale decomposition on the images by using a Gaussian pyramid and a Laplacian pyramid, and reconstructing and generating a final enhanced image after layer-by-layer fusion. According to the method provided by the invention, the advantages of brightness enhancement and color optimization are combined, and an image with balanced brightness, rich details and natural colors can be generated in a low-illumination scene.

The invention discloses a panoramic image splicing method, system and device, and relates to the technical field of image processing, and the method comprises the steps: collecting images around a vehicle at different visual angles; using the MASK masks to generate overlapping region masks of the different view angle images, and performing local feature detection on the overlapping region masks to generate a transformation matrix; aligning the images of different visual angles according to the transformation matrix; a Canny edge detection operator is fused in splicing seam energy detection, the energy weight of the image edge area of the aligned image is enhanced, and a splicing seam is guided to actively avoid an object with remarkable structural features; b spline interpolation is introduced to optimize an initial splicing seam path; constructing a Laplacian pyramid along Gaussian blur of a splicing seam, and sequentially carrying out gradual fusion to generate a seamless panoramic image; according to the method, the basic function of eliminating the visual blind area is realized, a clear and visual panoramic view can be provided for a driver, and the driving safety is effectively improved.

The invention belongs to the technical field of image processing, and particularly relates to a physical prior guidance-based interpretable underwater image enhancement method, which comprises the following steps of: constructing a physical prior guidance-based diffusion model which comprises a high-frequency prior generation branch and a physical information perception diffusion model enhancement branch; in the high-frequency prior generation branch, firstly, an input underwater image is decomposed by using a Laplacian pyramid to obtain a multi-scale high-frequency feature map, the multi-scale high-frequency feature map is integrated with an original feature map extracted from the underwater image, and then a transmission map, a global background light map and a defogged clear underwater image are generated by combining an atmospheric scattering model; the diffusion model enhancement branch of physical information perception comprises a cross attention module and a diffusion model, and the diffusion model guides the diffusion process through the cross attention module by using a physical attribute graph to obtain an enhanced image. According to the method, the high-frequency information of the underwater image and the original image features are fused, and the internal relation between different scale features is enhanced, so that the semantic consistency of feature representation is improved.

The invention relates to the technical field of video generation, in particular to a video frame generation system and display equipment based on texture reconstruction and trajectory optimization, and the system comprises a motion breakpoint detection module, a trajectory interpolation frame generation module, a texture anomaly positioning module, a texture interpolation reconstruction module and a video frame synthesis module. According to the method, an edge feature map is generated through edge detection and non-maximum suppression, the boundary recognition precision is improved, noise is suppressed, candidate regions are divided through region growth, the local feature extraction efficiency is enhanced, global redundancy is reduced, three-frame optical flow analysis captures trajectory breakpoints, and motion vector correction path deviation is predicted in combination with a Lucas-Kanade method. Color consistency verification optimizes interpolation to reduce cross-frame abrupt change; 64 * 64 grids and a gray level co-occurrence matrix locate texture abnormity; five-frame similarity strengthens time sequence stability; Gabor filtering extracts a main direction and combines an adjacent frame reconstruction texture direction to ensure consistency; and trajectory smoothness and visual consistency are optimized through multi-frame cooperation and layered detection.

The invention belongs to the technical field of image processing, and discloses an underwater dredging robot terrain recognition method and system, and the method comprises the steps: obtaining an illumination attenuation coefficient through the shooting depth and plankton information, accurately correcting the influence of illumination on an image, and carrying out the brightness adjustment of the image based on the illumination attenuation coefficient, so as to enable the terrain features to be more prominent. Utilizing Laplace decomposition to obtain a multi-layer topographic image, and realizing multi-scale analysis; the accuracy of terrain analysis is improved by screening an edge texture region; the defogging weight is determined in combination with the illumination attenuation coefficient, and the dark channel prior is adopted for defogging recovery, so that the defogging effect is effectively removed, and the image visibility is improved. And reconstructing and synthesizing a complete image through an inverse Laplacian pyramid to obtain an underwater topographic image of which the quality is comprehensively improved, and further obtaining underwater topographic information. The method not only improves the image quality, but also achieves the precise analysis and recognition of the underwater topography, and provides powerful technical support for the research of the underwater topography.

The invention discloses an underwater image enhancement method and system based on three-input multi-scale fusion. The method comprises the following steps: carrying out visibility recovery on an image to be processed; performing contrast enhancement through a nonlinear mapping function; carrying out enhancement processing on the contour of the high response area; respectively calculating a Laplacian contrast weight map, a saliency weight map and a saturation weight map of the three processed images; linearly combining all types of weight maps to obtain an aggregation weight map, and normalizing the aggregation weight to obtain a normalized weight map; decomposing the three processed images into a Laplacian pyramid, decomposing the normalized weight map into a Gaussian pyramid, and performing fusion through upward sampling and addition reconstruction; carrying out smoothing processing and edge-preserving sharpening processing on the fused image to obtain an image after underwater image enhancement and restoration; according to the method, the color cast problem of the underwater image is solved, the image contrast is effectively improved, and the texture details of the image are improved.

The invention discloses a multi-scale image segmentation reconstruction method and system applied to a remote sensing image, and relates to the field of image segmentation reconstruction, and the method comprises the steps: obtaining a high-definition remote sensing image of a to-be-detected region, and building a detection region high-definition remote sensing data image set; extracting color and contour features of a to-be-detected area, and establishing a standard feature map data set; based on a dark channel prior defogging algorithm, the influence of haze on the quality of the remote sensing image is removed; segmenting the collected remote sensing image based on the color and contour features of the to-be-detected area; calculating the ambiguity of each part of the remote sensing image, and judging the quality of each part of the remote sensing image; on the basis of a neural network model, replacing and reconstructing a part with poor quality of the remote sensing image; and based on a Laplacian pyramid algorithm, performing optimization processing on the remote sensing image after replacement and reconstruction processing. The method has the advantages that the output quality of the remote sensing image is effectively improved, and the influence of haze and partial region blurring or shielding is reduced.

The invention relates to the technical field of digital image processing, in particular to a real-time image quality enhancement method for a vehicle-mounted camera EMC test, and the method comprises the steps: obtaining real-time image data and historical image data; and performing multi-branch collaborative decoupling processing on the real-time image data to generate a frequency domain purification graph, a core structure graph, a space domain purification graph and a time domain stability graph. And performing local statistical analysis on the real-time image data to generate an artifact intensity map and a perception saliency map. Under a multi-resolution Laplacian pyramid framework, fusing the frequency domain purification graph and the space domain purification graph; the weight of the fusion process is intelligently regulated and controlled spatially and hierarchically by an artifact intensity graph, a perception saliency graph and a time domain stabilization graph which are constructed based on a Gaussian pyramid, and finally a high-quality enhanced image is reconstructed and generated. According to the invention, through a multi-branch cooperative decoupling and intelligent fusion method, real-time enhancement of the vehicle-mounted camera image under EMC interference is realized.

The invention discloses a self-adaptive fatigue state detection method based on multi-modal feature fusion, and belongs to the technical field of fatigue detection, and the method comprises the steps: synchronously collecting facial images through an infrared camera and an RGB camera, carrying out the image fusion through a Laplacian pyramid method, extracting facial features, setting a personalized threshold value, and obtaining fatigue features. Performing special scene judgment under limited face recognition, considering micro-expression features, performing fatigue parameter supplementation, judging a fatigue state through a comprehensive fatigue model with multi-fatigue feature fusion, and performing early warning information reminding; according to the adaptive fatigue state detection method based on multi-modal feature fusion, through multi-modal feature fusion, image details are effectively reserved, a low-light environment is adapted, facial feature deficiency is compensated, and the accuracy and stability of fatigue state detection are remarkably improved by combining personalized threshold values and special scene judgment.

The invention discloses a monocular structured light three-dimensional imaging method and system based on multi-frequency polarization projection, and belongs to the technical field of image processing. The method comprises the following steps: acquiring a structured light stripe image based on the surface of a high-reflection object, acquiring a plurality of multi-frequency polarization stripe images at different angles according to the structured light stripe image, and determining an overexposure area of each multi-frequency polarization stripe image; constructing a mask image of the multi-frequency polarization fringe image based on the overexposure region and obtaining a corresponding highlight image, constructing a corresponding Gaussian pyramid image sequence and a Laplacian pyramid image sequence, constructing a pyramid fusion image based on the plurality of Gaussian pyramid image sequences and the plurality of Laplacian pyramid image sequences, and obtaining a multi-frequency polarization fringe image; and obtaining a final reconstructed image according to the fused pyramid image, and combining the final reconstructed image with the structured light stripe image to obtain a target three-dimensional image. The method can effectively solve the problem that part of detail features are difficult to restore for the overexposure phenomenon.