399 results about "Edge enhancement" patented technology

The invention discloses an efficient target detection method in a foggy environment, and relates to the technical field of target detection. According to the method, a lightweight foggy day target detection model is established and called DF-DETR, a defogging module of a double-branch structure is designed through an edge enhancement module, and target features of a foggy day image are effectively captured; then, a dual convolution feature extraction module DualConv-Block is designed, so that feature extraction is enhanced, and meanwhile, the complexity and the calculation amount of the model are remarkably reduced; besides, an EAA attention mechanism is combined with an intra-scale feature interaction module to form an AIFI-EAA module, and the AIFI-EAA module is integrated into the hybrid encoder, so that the attention capability of the model on dense targets is improved, and missing detection and false detection are effectively reduced; finally, a dynamic sampling scale attention feature fusion module is designed, alignment of multi-scale features is achieved through dynamic up-sampling, the flexibility and robustness of feature expression are enhanced, and the fusion and expression ability of the multi-scale features is further optimized.

The invention discloses an industrial image change anomaly detection method and system based on artificial intelligence, and relates to the technical field of image recognition, and the method comprises the steps: collecting a dual-light-source industrial image, employing frequency domain saliency to guide fusion, and carrying out visual enhancement processing through color mapping and edge enhancement; inputting the enhanced image into a CNN convolutional network to generate a multi-scale feature map, extracting a multi-scale high-pass residual image through two-dimensional fast Fourier transform and a high-pass filtering template, and splicing all scales and coding to generate a token sequence through local attention guide fusion; constructing a self-induction visual model, and performing feature reconstruction on the token sequence to generate a reconstructed feature map; and calculating and reconstructing an error scoring graph by adopting double error indexes, sampling to obtain an abnormal smooth graph, and segmenting an abnormal region based on the abnormal smooth graph. And finally, a dual anomaly detection system of image-level judgment and region-level identification is constructed.

The invention relates to the cross technical field of computer vision and civil engineering, and discloses a deep learning-based bridge disease image segmentation method, which comprises the following steps of: establishing an image data set containing crack and spalling diseases and performing online enhancement; constructing a segmentation network model comprising a frequency dynamic convolution encoder branch, an edge enhancement Transform encoder branch, a gating cooperation unit, a decoder and a depth supervision module; training the model by using a weighted mixed loss function; and inputting the test set to obtain a final segmentation mask. Self-adaptive fusion of local texture features and global context information is realized through a dual-encoder architecture and a gating cooperation mechanism; a frequency dynamic convolution and edge enhancement module is utilized to enhance the anti-noise capability and micro-disease perception under a complex background; and in combination with a category weighting strategy, the problem of pixel category imbalance is effectively solved, and high-precision automatic segmentation of concrete bridge diseases is realized.

The invention discloses a seal removing and document repairing method based on quantum state collaborative regulation and control, and relates to the field of document image processing and quantum computing cross technology, the method comprises the following steps: obtaining to-be-processed information, and carrying out quantum-classical feature collaborative preparation; the character stroke continuity is guaranteed through quantum entangled state modeling and entanglement degree constraint iteration, texture decoupling is achieved through quantum wavelet transform and Gram-Schmidt orthogonalization, and adaptive filling is conducted in combination with a quantum generative adversarial network; dynamic quantum phase adjustment is used for counteracting superposition interference of stamps with different transparency, and quantum neural network noise reduction and multi-scale quantum Fourier sharpening are used for optimizing image quality; checking the repair result, if the repair result does not reach the standard, returning to the edge sharpening link to perform decoupling and filling the edge sharpening link to readjust the parameter; and for special scenes such as inclination, multi-color overprinting and ultra-thin frames, quantum rotation correction, color channel separation and boundary annihilation operator processing are used, finally, high-precision, high-naturalness and high-adaptability restoration of seal removal is achieved, and high fidelity of results is guaranteed.

The invention discloses a multi-scale SAR (Synthetic Aperture Radar) image ship detection method and system based on edge enhancement and diffusion denoising, and mainly solves the problems that the existing SAR ship detection method is sensitive to noise and poor in small target feature extraction capability. According to the implementation scheme, the method comprises the following steps: obtaining an SAR image, carrying out standardized preprocessing, inputting the SAR image into a deep convolutional neural network, extracting a multi-scale feature map, and carrying out dynamic channel fusion enhancement on a low-layer feature map in the multi-scale feature map to obtain a fused high-quality feature map; performing differential edge enhancement on middle and high-level feature maps in the multi-scale feature map, and inputting the enhanced feature map and the fused feature map into a diffusion model detection head for training; and inputting a to-be-detected SAR image into the trained diffusion model detection head, outputting a preliminary target bounding box and a category confidence coefficient, performing score screening and non-maximum suppression operation on the preliminary target bounding box and the category confidence coefficient, and generating a final ship target detection result. According to the method, the precision and robustness of SAR image ship detection are remarkably improved, and the method can be used for ocean monitoring and military reconnaissance.

The invention relates to the field of image processing, and particularly discloses a rural highway pavement disease intelligent identification and positioning system comprising an image standardization module used for obtaining a standardized grayscale image; the wavelet decomposition module is used for obtaining a low-frequency sub-band and a plurality of high-frequency sub-bands; the high-frequency processing module is used for carrying out soft threshold processing on the high-frequency coefficient and retaining high-variance region features; the inverse transformation module is used for reconstructing the de-noised image; the edge enhancement module is used for highlighting crack and pit slot target contour information; the binarization module is used for segmenting a foreground disease candidate region; and the edge filling module is used for separating the disease from the background. According to the method, the core contradiction between background impurity removal and disease feature retention in rural highway tiny disease recognition is effectively solved, a traditional denoising algorithm either smooths tiny disease features or cannot thoroughly remove background impurities, and the system achieves the balance of the background impurity removal and the disease feature retention through cooperation of multiple modules.

The invention discloses an unmanned aerial vehicle small target detection method based on feature enhancement and selective attention and a related device, and relates to the technical field of computer image target detection, and the method comprises the steps: obtaining an aerial image of an unmanned aerial vehicle, adjusting the image to a preset resolution, and obtaining an adjusted image; inputting the image into a backbone network of a network architecture, extracting a multi-level feature representation through a plurality of CALBlock feature extraction modules, and enhancing features of the multi-level feature representation through an EMIT edge enhancement architecture to obtain an enhanced multi-level feature representation; inputting the enhanced multi-level feature representation into a neck network of the network architecture, and performing cross-scale feature fusion and enhancement through an FSAFPN structure to obtain a multi-scale enhanced feature map; and inputting the multi-scale enhanced feature maps into a detection network of a network architecture, and performing target detection by using a decoder to obtain target category probability distribution and bounding box coordinates.

The invention discloses an SAR ship instance segmentation method for self-adaptive representation alignment, and belongs to the field of SAR image instance segmentation. According to the implementation method, the problems of semantic-structural feature mismatch, global-local feature extraction mismatch and cross-dataset scale generalization mismatch are solved through modular design by constructing an adaptive representation alignment network. The method comprises an edge-guided boundary optimization module, a context sensing module and a depth adaptive feature pyramid module. The boundary optimization module introduces edge enhancement information in the feature extraction process to improve the target boundary positioning precision; the context sensing module fuses a multi-path global attention mechanism in a deep feature stage, and the semantic discrimination capability is enhanced; the depth adaptive feature pyramid module adaptively selects the optimal feature fusion depth by constructing a multi-depth fusion path in combination with scale statistical information, and improves the cross-dataset robustness. According to the method, the segmentation precision of the SAR ship instance can be remarkably improved.

The invention discloses a feature reconstruction-based tiny target detection method and device, a server and a medium, and belongs to the technical field of target detection. Comprising the following steps: inputting a primary feature obtained by convolution of an image into a two-layer DRSS-Unit module for processing to obtain a shallow edge enhancement feature; inputting the shallow-layer edge enhancement features into an MGSP-Unit module for processing to obtain middle-layer semantic transition features; the middle-layer semantic transition features are input into an MGSP-Unit module to be processed, and deep-layer sparse semantic features are obtained; the deep sparse semantic features are input into an SDF-AIFI module to be processed, and frequency domain global enhancement features are obtained; inputting the frequency domain global enhancement feature, the shallow edge enhancement feature and the middle semantic transition feature into a CCFM module for feature reconstruction to obtain a reconstructed fusion feature; and inputting the reconstructed fusion feature into a decoder to obtain a tiny target detection result. By constructing a cascade feature reconstruction and enhancement module, multi-level features are gradually reconstructed and enhanced, and detection of a tiny target in a complex scene is realized.

The invention discloses a language-guided structure-aware network architecture and method for camouflage target detection, and relates to the technical field of camouflage target detection. According to the method, the text is guided to focus on a potential target, text semantic and visual features are fused through the CLIP model, the target mask is generated, the problems that a traditional model lacks semantic guidance and is difficult to focus on a camouflage area are solved, and background interference is greatly reduced; edge details can be accurately extracted, a Fourier edge enhancement module (FEEM) is combined with spatial domain edge enhancement and frequency domain high-frequency information capture, the problem of a fuzzy pain point of a camouflage target boundary is effectively solved, and the edge positioning precision is improved; structural and local dual optimization is proposed, a structural awareness attention module (SAAM) is fused with semantic and edge information, and a coarse guidance local refinement module (CGLRM) is designed through double branches of global guidance and local refinement, so that the regional consistency and structural integrity of a segmentation result are guaranteed, and local details are prevented from being lost.

The invention discloses an ultrasonic blood flow quantitative analysis method based on a skeletonized image, which comprises the following steps of: firstly, selecting a target analysis area containing a micro blood flow image in a grey-scale map formed by ultrasonic imaging; the method comprises the following steps of: firstly, carrying out normalization processing, nonlinear gray mapping, Gaussian filtering, minimum filtering and high-pass filtering in sequence to realize enhancement, noise suppression and edge enhancement on a target image, and finally, carrying out binaryzation on the image, and carrying out skeleton extraction based on an iterative refinement principle to obtain a final blood flow skeleton. According to the method, preprocessing such as noise suppression and edge enhancement is performed on the ultrasonic micro blood flow image before binarization and skeleton extraction, so that the accuracy and robustness of skeleton extraction are remarkably improved, and a reliable basis is provided for subsequent quantitative analysis and quantitative evaluation of a vascular network structure.

The invention provides a video content enhancement method for a low-illumination environment, and the method comprises the steps: achieving the data preprocessing based on an original low-illumination video frame sequence through frame synchronization, color space conversion and local brightness analysis, generating a noise sensitivity thermodynamic diagram through multi-feature unsupervised learning, and constructing a noise perception gating mechanism through the combination of affine transformation. Dynamic modulation of the characteristic channel is realized; in the multi-scale network structure, a channel attention module is used for carrying out layer-by-layer self-adaptive adjustment on a noise sensitive area; a basic illumination image and an edge enhancement image are generated through double-branch decoding, and then weighted fusion is carried out in combination with a noise thermodynamic diagram, so that brightness balance and detail enhancement are realized; a noise smoothing regular term is introduced during end-to-end training, so that the network achieves dynamic balance between an enhancement effect and noise control.

The invention discloses an artemisinin extraction intelligent tracking and identification method, and relates to the technical field of image data processing. According to the method, an integrated closed-loop system is constructed through cooperation of four core technical means: based on classified filtering of foam stability difference and bimodal adaptive segmentation, dynamic foam is accurately removed, and preliminary interface extraction is optimized; the attention enhancement U-Net network is embedded into a channel attention module and an edge enhancement branch to realize accurate positioning and fluctuation tracking of a layered interface; the attention mechanism dynamically adjusts the bimodal weight, and completes multi-index cooperative tracking in combination with a partitioning strategy and an improved network; a crystal growth model is introduced to correct particle weight, a tracking effect is optimized by adopting layered resampling, and a closed-loop feedback mechanism of a crystal state and process parameters is established. All technical means are deeply coordinated, intelligent and accurate management and control of the whole extraction process are achieved, and reliable technical support is provided for artemisinin extraction production.

The invention provides a remote sensing image change detection method based on a CNN-Mama hybrid network, and relates to the technical field of image detection, and the method comprises the following steps: carrying out the preprocessing of a disclosed remote sensing image change detection data set, and constructing a dual-temporal high-resolution remote sensing image training set and a dual-temporal high-resolution remote sensing image test set; the preprocessing comprises image registration, cutting, normalization and data enhancement; constructing a change detection network model based on a CNN-Mama trunk, and performing training on the training set to obtain an optimal model; the network model comprises a twin encoder, a dynamic grouping attention module, a space adaptive fusion module, a multi-scale edge enhancement module and a decoder; and inputting a dual-temporal remote sensing image of a to-be-detected area into the optimal model, and outputting a binary change detection image with the same size as the input image through feature extraction, feature interaction, cross-layer fusion and step-by-step decoding. According to the method, the convolutional neural network and the state space modeling network are combined, and the local feature capture capability of the CNN and the long-range dependence modeling characteristic complementation of Mama are combined.

The invention provides an adaptive weight distribution chromosome stripe contrast enhancement method and system, and the method comprises the steps: firstly improving the significance of a chromosome stripe structure and an edge contour in a chromosome gray image through an edge enhancement convolution operator; carrying out chromosome monomer target detection and instance segmentation on the metaphase chromosome based on an instance segmentation network of deep learning to obtain a chromosome monomer ROI region; then, extracting a structure tensor response of the ROI region of the chromosome monomer, designing an adaptive enhancement weight of a histogram merging interval based on a normalized difference between a response value and global statistical information, and performing reconstruction and interpolation on a histogram of the image of the ROI region of the chromosome monomer; and finally, adaptively enhancing the contrast of the chromosome stripe through iterative histogram matching. According to the method, the original image texture structure is kept, and meanwhile, the definition and the distinguishability of chromosome stripes are effectively and adaptively improved.

The invention discloses an SPR (Surface Plasmon Resonance) image optimization processing method based on image segmentation and edge enhancement, which comprises the following steps: acquiring SPR image data, and preprocessing to generate standardized SPR image data; inputting a structure boundary extraction model constructed based on CGNet, generating a structure boundary label graph, and aligning the structure boundary label graph with the image; gradient amplitude and local entropy mutation detection is executed, and an artifact guide graph is generated; respectively inputting the standardized image into details and context branches of the improved CSDNet, and extracting edge and semantic feature maps; inputting a guide perception gating module, executing structure enhancement and artifact suppression fusion, and generating a fusion feature map; inputting into a multi-scale detail recovery module, and outputting an edge enhanced image; and executing structural similarity and marginal definition scoring based on the original image and the enhanced image, and generating an optimization result. According to the method, synchronous optimization of SPR image edge enhancement and structure maintenance is realized, and the image definition and diagnosis availability are remarkably improved.

The invention discloses a road facility disease evolution prediction and maintenance decision-making method and system based on a CNN-Transform hybrid architecture. The method comprises the following steps: acquiring a multi-period aerial image, constructing a time sequence data set, and carrying out spatial alignment and simulation degradation processing; a hybrid network model of an encoder-decoder structure is constructed, an encoder adopts a dual-channel structure, local features are extracted through a convolution block, global semantic features are extracted based on an attention mechanism and integrated with an edge enhancement component, and a decoder adopts a bidirectional propagation unit and fuses the features through a cross-scale attention mechanism; carrying out cooperative training and compression on the model to obtain a simplified prediction model; predicting a facility state by utilizing the model, constructing a multi-criterion optimization target, and solving by adopting a genetic algorithm to obtain a non-dominated solution set; and finally, generating a maintenance decision report according to the decision preference and visually displaying the maintenance decision report. According to the invention, accurate prediction and scientific maintenance decision making of road facility disease evolution are realized.

The invention belongs to the technical field of optical imaging, and discloses a multifunctional image edge enhancement device based on spiral phase contrast imaging, which comprises a light source system, an optical imaging system and an edge enhancement detection system which are arranged in sequence, the light source system is used for generating collimated linear polarization laser; the optical imaging system comprises a first 4-f system and a round hole diaphragm which are sequentially arranged on a light path; a sample to be tested is arranged between the light source system and the first 4-f system; the edge enhancement detection system comprises a first lens, a vortex wave plate, a second lens and an imaging unit which are sequentially arranged on a light path; the first lens and the second lens form a second 4-f system; linearly polarized laser output by the light source system is transmitted by a to-be-detected sample, enters the first lens after passing through the first 4-f system and the round hole diaphragm, and then is imaged on the imaging unit after passing through the vortex wave plate and the second lens in sequence. According to the invention, edge enhancement can be realized, and the contrast and resolution of the image are improved.

The invention belongs to the technical field of slope monitoring and early warning, and provides a slope progressive failure precursor identification method based on deep learning. On the basis of 4D point cloud imaging radar data, surface patches are divided according to slope sections, rock stratum boundaries and the like, attributes are marked, a directional kernel is constructed in the dominant direction and the normal direction, and density, flatness, roughness, edge feeling, pore feeling and other characteristics and state labels are extracted through independent coding of all moments; constructing a normal learning window and a usual trajectory based on environmental conditions, training a conditional variation auto-encoder which only learns a normal form, setting two interpretable dimensions of pore evolution and edge enhancement, and applying direction consistency and state constraint; according to the reconstruction deviation degree and the consistency of adjacent surface patches, the abnormal trend that the density is reduced, the roughness / edge is increased and the pores are increased in the dominant direction is recognized online, and encryption observation is triggered for side slope precursor recognition and early warning; the method does not depend on cross-time displacement regression, and the influence of environmental disturbance can be inhibited.

The invention discloses a lightweight target detection method and system fusing edge enhancement and semantic interaction. The method comprises the following steps: utilizing an edge sensing backbone network comprising an edge residual enhancement unit (EREU) and a progressive edge refinement network (PERN) to reinforce edge structure information in a multi-scale feature map; dynamically aggregating features of different levels by using a bidirectional attention guidance feature fusion module (BAFFM); and decoding the fused features and outputting target information. The invention also discloses a corresponding target detection system. The system comprises an image acquisition module, a data processing module and a result display / early warning module. Wherein the data processing module can be an edge computing device and is communicated with a central control server; the result display / early warning module can trigger an alarm signal according to preset rules such as the real-time position, behavior or time of the target. According to the method, the small target detection precision is improved, and a set of efficient deployment scheme containing intelligent early warning logic is provided.

The invention relates to the technical field of image processing, and particularly discloses a strong reflective weld image anti-interference enhancement system and method and a medium. The system comprises an image acquisition module, a physical reflection characteristic analysis module, a self-adaptive illumination component separation module, a characteristic retention type light reflection suppression module and an image fusion output module, and by analyzing the reflection characteristics of the surface of a welding seam, separating an illumination layer from a reflection layer, implementing gradient-guided brightness compression and edge enhancement, and performing dynamic weight fusion, the image fusion output module is used for performing image fusion. And strong reflection inhibition and weld seam detail enhancement are realized. According to the invention, the image quality and robustness of weld defect detection can be improved.

The invention provides an intelligent interpretation method for a dual-temporal remote sensing image, which is characterized in that a boundary constraint change detection model BCnet based on a visual basic model is constructed by the method, the universal semantic representation potential of the visual basic model is fully excavated, and a difference detail enhancement module and a multi-scale edge enhancement module are introduced, so that the visual basic model can be fully interpreted. The problem that high-frequency information is lost in the direct migration process of the visual basic model is solved, and fine description of tiny changes and complex textures is achieved. On the basis, an edge feature constraint strategy is designed, an edge feature aggregation module is combined, boundary supervision signals are introduced into a feature space and a prediction space at the same time, and the continuity and geometric integrity of a change detection result in the space structure are enhanced. Experimental results prove that the BCnet has the advantages of high robustness and precision in high-difficulty scenes such as dense building groups and complex edges.

The invention discloses a fresh tea leaf sorting method based on a frequency domain tree topology network, computer equipment and a storage medium. The method covers the complete process of image acquisition, preprocessing, frequency domain decomposition, deep modeling, map construction and classification. Firstly, image quality is improved through color normalization and edge enhancement, and frequency domain tree decomposition is carried out through wavelet transform and discrete cosine transform to extract multi-scale features. And then, fusing long and short range dependent modeling and a residual convolution module to realize multi-level feature representation, and constructing a tree topology attention path and a structure map for simulating a bud-leaf-vein relationship to enhance semantic understanding. A tree structure is adopted to perceive a classification function, and fine-grained classification of single bud, one bud and one leaf, one bud and two leaves and one bud and multiple leaves is achieved. In training, the robustness of the model is improved by combining cross entropy loss, data enhancement and a regularization strategy. The method is high in classification accuracy and good in stability on a plurality of tea image data sets, and the practical level of automatic fresh tea leaf sorting is effectively improved.

The invention provides a welding seam parameter anti-interference measurement method, which comprises the following steps of: acquiring spectrum characteristics of a high-reflectivity surface through a laser scattering technology and Fourier transform processing, matching a correction coefficient, realizing weighted adjustment and geometric parameter extraction of a scattering signal, accurately quantifying a groove size in combination with an edge enhancement algorithm and a three-dimensional contour modeling technology, and accurately measuring the welding seam parameters. Interference such as surface oil stain, corrosion and stripe fracture is effectively overcome by using image filtering, morphological reconstruction and an abnormal value elimination algorithm, and finally, high-precision and anti-interference measurement of the weld groove angle, the truncated edge thickness and the assembly alignment tolerance under high-reflection, corrosion and complex working conditions is realized through multi-source data fusion and self-adaptive calculation range adjustment. And the reliability and the automation level of welding quality detection are remarkably improved.

The invention discloses an SAR ship detection method based on multi-scale edge information enhancement and a lightweight decoupling detection head, and relates to the field of image detection. According to the method, an SAR ship detection model comprising an edge enhancement wavelet backbone network, an adaptive context focusing feature pyramid network and a lightweight decoupling detail enhancement detection head is constructed, and multi-scale feature extraction, fusion and target identification are performed on an input SAR image. According to the method, the edge feature extraction capability and the small target detection precision of the ship target in the SAR image are effectively improved, higher robustness and detection efficiency are achieved under complex sea conditions and noise interference, meanwhile, the model parameter quantity and the calculation complexity are controlled, and actual deployment and application are facilitated.

The invention discloses a multi-mode human body recognition system and method based on photoelectric metasurface and radar fusion, and belongs to the technical field of optoelectronics and radar perception. According to the system, optical edge enhancement is realized through a phase programmable metasurface, a continuous wave radar and a frequency modulation continuous wave laser radar are combined to obtain a micro-Doppler spectrum and a three-dimensional point cloud, and a near-infrared reflection spectrum is collected at the same time. According to the algorithm, a cross-modal Transform-GAT architecture is adopted to fuse multi-source features, edge extraction is optimized through a self-adaptive kernel function, and low-power-consumption edge reasoning is achieved through a Mach-Zehnder interference device or a ReRAM chip. Experiments show that the recognition accuracy of the method in complex scenes such as multi-target shielding and low light is remarkably superior to that of a single-mode scheme, the method has the edge deployment capability with delay smaller than or equal to 25 ms and power consumption smaller than or equal to 4 W, and an efficient solution is provided for multi-scene human body dynamic recognition and health monitoring.

The invention provides a high-quality three-dimensional grid reconstruction method which comprises the following steps: feature extraction and three-dimensional initial estimation: extracting a multi-scale feature map from an input image through a CNN backbone network; a UV expansion optimization module; illumination decoupling and material recovery are carried out; enhancing high-frequency geometric details; and performing end-to-end training and output. According to the invention, adaptive alignment of the texture space and the grid topology structure is realized, the problems of mapping stretching, splicing dislocation and the like in a traditional UV projection mode are effectively solved, illumination components in an image are explicitly separated by constructing a low-frequency spherical harmonic illumination modeling and high-frequency illumination residual error compensation module, the real texture of an object is accurately recovered, and the quality of the object is improved. The robustness and mobility of the model to different illumination environments are improved, local refinement and edge enhancement processing are carried out on the three-dimensional model by using a normal graph and grid curvature information predicted from an image, and the modeling capability for a high-frequency structure is enhanced while the overall shape stability is kept.

The invention discloses an image flower character segmentation method, device and equipment and a readable storage medium, and is applied to the technical field of computer vision, and the method comprises the steps: obtaining a to-be-segmented flower character image and an improved deep learning model; extracting a multi-scale feature map of the to-be-segmented flower character image by using an encoder of the improved deep learning model, and fusing the multi-scale feature map by using a multi-scale feature fusion mechanism to obtain a fused feature map; and weighting the fused feature map by using a double-path attention mechanism to obtain an attention weighted feature map, and performing edge enhancement while performing up-sampling on the attention weighted feature map by using a decoder to obtain a target flower character segmentation map. Interference of complex backgrounds can be suppressed through a double-path attention mechanism and a multi-scale feature fusion mechanism in the model, and fine structures and edge details of art fonts are accurately captured based on a designed loss function and an edge enhancement mechanism, so that robust and high-precision segmentation of the patterns is realized.

The invention discloses a PCB bare board defect detection method and system based on an improved YOLOv11 neural network, and the method comprises the steps: obtaining a PCB defect data set, carrying out the preprocessing of an image of the data set, and constructing a PCB surface defect data set; an improved YOLOv11 detection model is constructed, in the Backbone stage, the depth separable convolution of improved PPLCNet and an SE module are utilized to reduce the calculated amount and enhance feature expression, in the Neck stage, multi-scale information fusion is optimized through cross-scale feature splicing and SPPF pooling, in the Head stage, an improved ShapeIoU loss function is introduced, and an improved edge enhanced EE-SimAM attention mechanism module is inserted in front of the object; training the model by using the preprocessed data set, and optimizing the initial weight by adopting a migration analysis strategy; and deploying the trained model to an edge detection device. The method has the beneficial effects that by constructing and improving the YOLOv11 network model, the size of the model is reduced, the model can be conveniently deployed to an edge equipment end, the PCB defect detection precision is improved, the omission ratio is reduced, and the method is suitable for industrial defect detection scenes.

The invention discloses a laparoscopic surgery image enhancement and real-time auxiliary system, and relates to the technical field of medical image processing and intelligent auxiliary surgery. According to the laparoscopic surgery image enhancement and real-time auxiliary system, a laparoscopic image flow is obtained and cleaned through the video input and preprocessing module, image enhancement processing is carried out through a deep learning model, then spatial position features of a tissue structure and an instrument are extracted, and a structure recognition index and a spatial safety index are analyzed; according to the invention, through cooperative work of the image preprocessing module and the depth image enhancement model, brightness compensation, edge enhancement, denoising, smoke suppression and other operations are performed on the laparoscope image stream, the depth model is utilized to extract multi-scale image features and reconstruct details, and an enhanced video image stream is generated; therefore, the intraoperative abdominal cavity structure and instrument boundary is clearer, the positioning judgment precision of a doctor is remarkably improved, and the misjudgment risk caused by image blurring is reduced.