8057 results about "Image enhancement" patented technology

The invention discloses a target identification tracking method and system based on multi-source fusion imaging, and the method comprises the steps: obtaining and preprocessing multi-modal environment monitoring data, carrying out the image enhancement based on the preprocessed multi-modal environment monitoring data, and obtaining the image-enhanced multi-modal environment monitoring data; performing multi-modal feature extraction on the image enhancement multi-modal environment monitoring data, and introducing a cross attention mechanism to fuse the extracted multi-modal features to construct cross-modal fusion features; constructing a cascade target recognition model, and inputting cross-modal fusion features to perform target recognition on the current monitoring scene; tracking a scene recognition target in the current monitoring scene to generate scene target tracking information, and storing the scene target tracking information in a preset track memory pool; and when a tracking target disconnection condition occurs during target tracking, obtaining a mismatched tracking trajectory, and performing tracking trajectory disconnection repair in combination with the trajectory memory pool. Therefore, the identification accuracy and tracking reliability of the target in the monitoring scene are improved.

Since defect areas in collected hydro turbine defect images account for a small proportion and there is a significant color difference between surrounding background and defects, which creates too much redundant information and greatly affects detection speed, a method for detecting defects in a hydro turbine top cover based on an improved YOLOv8 model involves first cropping collected defect images to obtain cropped images, then expanding the cropped images by image enhancement techniques such as image flipping and image blurring to obtain a training dataset. A defect detection network based on YOLOv8-CBAM is constructed and trained to generate a defect detection model for detecting the defects in the hydro turbine top cover. The method achieves high precision in defect detection for the hydro turbine top cover.

The invention provides a semiconductor chip defect automatic detection method and system based on hyperspectral imaging, and relates to the technical field of chip detection, and the method comprises the steps: collecting a chip hyperspectral image, carrying out the image enhancement and illumination non-uniformity correction through an improved neuromorphic adaptive processing network and a dual-path diffusion model, and carrying out the detection of the hyperspectral image. Extracting multi-level features by using a dynamic mixed feature enhancement network based on neural architecture search, fusing the features through a cross-layer attention mechanism, generating an initial defect detection result by using a multi-branch expert decision-making module and an uncertainty dynamic integration strategy, constructing a space-time knowledge inference graph, and determining a defect detection result; and performing reliability evaluation and optimization on the initial defect detection result in combination with the form similarity, the spatial position relationship and the process parameter influence model to obtain a final defect detection result.

The invention discloses an unmanned aerial vehicle autonomous routing inspection conductor detection method and system based on Bezier curve modeling. The method comprises the steps that a binocular image sequence, GPS positioning and IMU attitude data are collected through an unmanned aerial vehicle, synchronous alignment processing is carried out on multi-source data, a scene dynamic evaluation value is calculated, adaptive image enhancement is executed, and space-time features are extracted and fused. Constructing a multi-scale feature pyramid, extracting wire topological features, and generating enhanced features in combination with environment context information; and generating a control point candidate set based on the enhanced features, optimizing Bezier curve parameters, and performing time sequence consistency optimization. According to the invention, high-precision lead detection and robust tracking in a complex scene are realized, and the reliability of autonomous inspection of the unmanned aerial vehicle is improved; the key technical problems of scale change, feature matching, dynamic modeling, data fusion, parameter optimization and the like in the wire detection process can be solved.

The invention provides an industrial part defect detection method, which comprises the following steps: acquiring an input industrial part burr defect image, extracting the shape, size and distribution position characteristics of burrs in the image to obtain a burr characteristic pattern, segmenting a burr defect region according to the edge roughness and color difference in the burr characteristic pattern, and generating a binary burr defect mask; performing morphological operation on the binarized burr defect mask, eliminating isolated noisy points through expansion and corrosion, connecting adjacent burr areas, segmenting the processed burr mask, and extracting independent burr defect areas according to the three-dimensional contour and surface texture features of burrs; performing image enhancement processing on the preliminary verification code character matrix, adjusting the color contrast of characters to highlight the thickness of character strokes, and generating a verification code image; and carrying out compression and format conversion on the generated verification code image to obtain a final industrial part burr defect verification code which is used for defect detection of workpiece parts.

The invention discloses an image enhancement method and system in a complex coal mine environment, and relates to the technical field of image processing, and the method comprises the steps: carrying out the preprocessing of a collected coal mine image of a target region, and dividing the coal mine image into different semantic regions, including a bright region, a dark region and a dust shielding region, through a deep learning semantic segmentation model; according to semantic region characteristics, a differentiation enhancement strategy is made; a traditional Retinex model is improved, non-local mean filtering is introduced, and an illumination component and a reflection component are decomposed through pixel similarity matching. According to the method, the image is divided into the bright area, the dark area and the dust shielding area through the deep learning semantic segmentation model, differential enhancement strategies are formulated according to different area characteristics, detail distortion caused by global adjustment is avoided, local contrast suppression is adopted in the bright area, illumination compensation is enhanced in the dark area, and the image quality is improved. Noise diffusion of the dust shielding area is inhibited through edge preservation smoothing, the image quality of each area is remarkably improved, and it is ensured that image details in a complex coal mine environment are clear and visible.

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

The invention provides a printing piece defect detection method and system based on machine vision, and relates to the technical field of machine vision, and the method comprises the steps: determining an optimal detection scale through image enhancement processing, multi-scale gradient pyramid construction and mutual information calculation, extracting key points through a curvature value, and carrying out the shape discrimination. And finally, generating a laser etching compensation path based on defect characteristics and performing layered compensation processing. According to the method, the automobile part surface printing flaws can be accurately identified, the compensation strategy is adaptively generated according to the defect types, and the defect repairing precision and efficiency are improved.

The invention discloses a manipulator grabbing method based on deep learning target detection and image segmentation, and relates to the technical field of artificial intelligence and robotics.The manipulator grabbing method comprises the following steps that a scene image to be processed is collected, the image quality is improved through the multi-light-source fusion image enhancement technology, and recognition errors caused by uneven illumination are reduced; and inputting the enhanced image to a pre-trained deep learning model, executing a target detection task, and outputting an initial bounding box and a category label of the target object. According to the method, through multi-light-source image enhancement and high-precision image segmentation, the accuracy of target recognition and contour extraction is remarkably improved, and the capture failure rate caused by image misjudgment is reduced. And meanwhile, geometric consistency verification and a multi-factor grabbing scoring mechanism are introduced, dynamic screening and collision pre-detection are conducted on the paths, the grabbing stability and safety of the mechanical arm in the complex environment are effectively guaranteed, and the intelligence and robustness of the whole system are remarkably improved.

The invention belongs to the technical field of intelligent navigation, and discloses a navigation obstacle avoidance method and system in a low-confidence and feature similar environment. The method comprises the following steps: acquiring multi-modal sensor data of obstacles in a detection area in a preset low-confidence environment by using a sensor; preprocessing the data of the multi-modal sensor; introducing a generative adversarial network to carry out image enhancement processing on the denoised image; performing cross-domain fusion on the data by using a converter architecture; obtaining an obstacle position, an obstacle type and a confidence degree distribution value based on a deep neural network, and constructing and obtaining an obstacle confidence degree map; dynamically planning an obstacle avoidance path by combining a deep reinforcement learning algorithm based on an obstacle confidence map obtained in real time, and performing auxiliary planning of the obstacle avoidance path based on an MPC model; the method can effectively cope with high challenge scenes such as tunnels and is widely applied to the fields of industrial automation, intelligent transportation and the like.

The invention discloses a medical image enhancement method and system based on multi-modal fusion, and the method comprises the steps: obtaining medical image data which comprises a CT image, an MRI image, a PET image and an ultrasonic image; calculating a mutual information value of each semantic tag in the cross-modal feature vocabulary, and generating an inter-modal feature correlation matrix; performing non-rigid space registration on the unified dimension image set to generate a geometrically consistent multi-modal image set; extracting skeleton region features, soft tissue region features and high metabolism region features from the multi-modal image set, and generating a core feature set; a deep learning algorithm is adopted to train the core feature set, and a segmented image set containing a segmentation mask is generated; and dynamically adjusting the fusion weight according to the regional features of the segmented image set, and generating a fusion enhanced image. According to the invention, through feature extraction, spatial registration and deep learning fusion of the multi-modal medical image, effective integration of different-modal medical image information is realized.

The present application discloses a low-light image enhancement method based on wavelet transform and Retinex-Net, and belongs to the field of image enhancement technology. In order to solve the problems of colour distortion and lack of details such as edges and textures when Retinex-Net processes some images, the present application adds the wavelet transform, fuses the high-frequency components with regional characteristics, and processes only the low-frequency component with Retinex-Net, to retain more details of edges and textures in the image, and transfers the low-frequency and high-frequency components from the HSV space to the value space for value fusion and stretching, to effectively improve the colour distortion of the image, and also improve the expression of details of the image. The present application retains more detail information while avoiding colour distortion, and the enhancement effect is satisfactory.

The invention relates to a feature enhancement-based multi-modal image-text tampering detection and positioning method, which comprises the following steps of: obtaining a tampered image-text pair, respectively preprocessing an input image and an input text, and constructing a data set; constructing a multi-modal image-text tampering model, wherein the multi-modal image-text tampering model comprises a text encoder, an image encoder, an image frequency domain enhancement module, a text enhancement module, a multi-cross attention fusion module and a multi-task tampering detection and positioning head; the image frequency domain enhancement module is used for selecting an optimal frequency band and sending the optimal frequency band out of the module; performing feature fusion on the frequency domain feature output by the image frequency domain enhancement module and the image domain feature output by the image encoder to obtain an image enhancement feature; fusing the aligned image enhancement features and text enhancement features by using a multi-cross attention fusion module; and training a multi-modal image-text tampering model by using the data set, and detecting and positioning multi-modal image-text tampering. According to the invention, the feature extraction capability of the network is enhanced, and the tampering detection and positioning capability is improved.

Approaches presented herein are directed to generating synthetic images with one or more augmentations realistically added to objects in the images, while ensuring the preservation and integrity of semantic or contextual information within the image. A synthetic augmentation system may identify and extract foreground image data (e.g., text), and a version of the image with the foreground image data removed can be processed by a generative diffusion model. One or more inputs can be provided to specify aspects such as a type or strength of augmentation to be performed. After an augmented image is generated using the generative diffusion model, the previously removed text can be blended back into the image. A synthetic augmentation system may use one or more blending weights for the text, such as a defect blending weight and a letter blending weight. The final result is a synthetic image with added realistic augmentations that preserves the semantic content.

The invention provides an underwater image enhancement method combining high-frequency enhancement, space-frequency mixed feature extraction and a Mama enhancement fusion technology. The method aims at solving the problems of low contrast ratio, color distortion, detail loss and the like caused by absorption and scattering effects in the light propagation process of the underwater image, the image quality is improved, and the visual perception effect is improved. The method specifically comprises the following four main steps: firstly, acquiring and preprocessing initial data sets of different underwater scenes; secondly, extracting texture detail information of the image through a texture enhancement module; thirdly, further enhancing texture details by using a space-frequency mixed feature extraction module; and finally, in combination with the original image, the texture information and the spatial frequency domain enhancement result, realizing feature fusion of the image through a Mama enhancement fusion module, thereby effectively recovering details of the underwater image, correcting color deviation and improving the definition and visual effect of the image. According to the method, the enhancement performance of the underwater image in a complex environment can be improved, and powerful support is provided for applications such as underwater detection, target detection, object recognition and image segmentation.

The invention discloses a weldment welding seam automatic detection method and device based on machine vision, and relates to the technical field of machine vision intelligent detection. The weldment welding seam automatic detection method and device based on machine vision comprises the steps that S1, surface images and forming feature data of a weldment are collected and preprocessed to construct a standardized image feature data set; s2, the boundary clearness of the weld joint is evaluated by combining the edge strength and the contour coherence, and the main contour extraction range is dynamically adjusted; s3, analyzing abnormal focusing characteristics of the candidate area, and adjusting a defect labeling range and a detection priority; and S4, integrating the boundary definition and the abnormal focusing features, analyzing the structure abnormality, and dynamically controlling and verifying a resource allocation strategy. The problems that in the weldment detection process, obvious light reflection and texture blurring phenomena exist in a heat affected area at a weld joint, a traditional image enhancement and edge extraction algorithm is difficult to stably recognize microdefects, and the credibility of a detection result is reduced are solved.

The invention relates to an underwater image enhancement method based on frequency domain analysis and visual Mama, comprising the following steps: constructing a multistage wavelet transform network, the network being an encoder-decoder structure, the encoder comprising a space-frequency feature fusion module and discrete wavelet transform; the decoder comprises a space-frequency feature fusion module and inverse discrete wavelet transform; an underwater image is decomposed into a low-frequency part and a high-frequency part through discrete wavelet transform in an encoder, spatial feature extraction and frequency feature extraction are conducted on the low-frequency part and the high-frequency part through corresponding space-frequency feature fusion modules based on Mama and frequency domain convolution respectively, and splicing is conducted on the low-frequency part and the high-frequency part along a channel in the decoder according to the sequence of low frequency and high frequency. Reconstruction is carried out through inverse discrete wavelet transform; and inputting the underwater image data into the multistage wavelet transform network to obtain enhanced underwater image data. Compared with the prior art, the method has the advantages of improving the enhancement quality, reducing the complexity, quickly enhancing and the like.

The invention discloses a two-stage low-illumination image enhancement method based on wavelet transform. According to the method, a two-stage U-shaped low-illumination image enhancement network is utilized, and deep extraction of image features and integration of multi-scale information are carried out on a low-illumination image; inputting the image features into a wavelet domain feature fusion and enhancement module, capturing image features of different scales on a wavelet domain by using discrete wavelets, and enhancing important features by using dynamic gating space attention and lightweight fusion curve attention; a fusion perception function and a lightweight edge loss function are constructed, the fusion perception function considers the pixel-level error and the overall perception quality of the image, and the model is guided to generate a more natural and highly realistic image by combining the pixel loss and the characteristics of the perception network; the lightweight edge loss function focuses on a high-frequency texture structure, and the reconstruction capability of the model on details is further optimized.

The invention discloses an insulator defect detecting and positioning method and system based on cross-scale feature fusion, and belongs to the field of intelligent routing inspection of power distribution network lines, and the method comprises the following steps: S1, obtaining image data containing a plurality of insulator defects of the power distribution network lines in real time, and carrying out the preprocessing of the image data; s2, image enhancement; s3, fusing the enhancement results under multiple scales obtained in the step S2 by adopting a pyramid fusion strategy to obtain a fused image; s4, inputting the fused image into a pre-trained cross-scale feature fused defect detection model, and outputting defect features; and S5, based on the detection time and the real-time speed of the defect features, considering the delay time, and predicting the position coordinates of the defect features. By the adoption of the insulator defect detection positioning method and system based on cross-scale feature fusion, high precision, high robustness and self-adaptive processing of insulator defect detection are achieved, and the method and system have important significance on intelligent detection and maintenance of insulator defects of a power distribution network line.

The invention relates to the technical field of agricultural knowledge base retrieval, and discloses an intelligent retrieval system for a knowledge base, which comprises an image preprocessing unit, an image adjusting unit, an image matching unit and a retrieval judgment unit, and is characterized in that a binary mask matrix is generated through image enhancement and pixel clustering, a modular entropy value is calculated in combination with parameters, and a basis is provided for feature extraction; multi-scale feature self-adaptive extraction is achieved through dynamic scale adjustment, feature distortion is avoided, feature points are evenly distributed through density dynamic adjustment and quality evaluation, high-quality feature points are screened, double screening of matching pairs is achieved by constructing a comprehensive evaluation mode, and the defects of an SIFT algorithm are overcome by dynamically adjusting a similarity threshold value and sorting. And the accuracy of image retrieval in a complex scene is improved.

The invention discloses a face image enhancement and recognition method in a low-light environment, and relates to the technical field of face recognition. Firstly, multi-scale Retinex enhancement is carried out on a low-illumination face image, brightness and details are improved, then LBP and HOG features of the enhanced image are extracted, weight fusion is dynamically adjusted according to illumination, then space and channel weighted optimization is carried out on the fused features by using a residual attention module, finally, optimized features are input into a pre-training model to extract vectors, and finally, the pre-training model is used for pre-training. And through cosine similarity matching identification, face processing and identity determination under low illumination are completed. According to the method, the problem of low-illumination face recognition is solved, the image quality is improved through multi-scale enhancement, feature extraction is enhanced through dynamic fusion and an attention mechanism, and the recognition accuracy and stability are improved; in addition, living body detection is added to guarantee safety, and the method is suitable for multiple scenes such as security and protection, finance and the like.

The invention discloses a super-resolution image enhancement system and method based on a variational mode decomposition algorithm. The system comprises an adaptive decomposition module, an enhancement processing module, a fusion module and an optimization module. The adaptive decomposition module receives low-resolution image signals, generates modal component signals containing different frequency band characteristics, and outputs modal quantity parameter signals according to image frequency domain energy distribution. The enhancement processing module comprises a high-frequency enhancement unit and a low-frequency reconstruction unit, and generates a high-frequency enhancement signal and a low-frequency reconstruction signal. And the fusion module receives the modal quantity parameter signal, the high-frequency enhanced signal and the low-frequency reconstructed signal, and performs spatial adaptive weighted fusion on the high-frequency signal and the low-frequency signal through a dynamic weight coefficient to generate an initial high-resolution signal. And the optimization module carries out adaptive nonlinear filtering processing on the initial high-resolution signal. The super-resolution image enhancement system based on the variational mode decomposition algorithm can solve the problem that the prior art is difficult to adapt to a complex image structure.

The invention discloses a wall crack detection system based on image recognition, which relates to the technical field of building monitoring and comprises an image enhancement denoising module, a geometric feature extraction module, a structure health prediction module, a crack classification rating module, a crack extension monitoring module, a repair suggestion generation module and a crack detection verification module. According to the wall crack detection system based on image recognition, through adoption of crack geometric feature extraction, a structure health prediction model based on a graph convolutional network and a crack expansion monitoring module combining time sequence analysis and an optical flow method, the precision and real-time performance of wall crack detection are remarkably improved; trend prediction and self-adaptive feedback of crack expansion are realized, an efficient and intelligent solution is provided for health management of a building structure, and potential structural failure is effectively prevented.

The invention discloses an image semi-supervised semantic segmentation method and system based on pixel-level correction, and relates to the technical field of computer vision and semantic segmentation, and the method comprises the steps: constructing a training data set which comprises a labeled image and an unlabeled image, and the labeled image is an image labeled with a pixel-level label; carrying out random diversity image enhancement to obtain a weakly-enhanced labeled image, a weakly-enhanced unlabeled image and a strongly-enhanced unlabeled image; constructing a teacher-student model; training a teacher-student model by using the training data set, constructing a total loss function based on a supervised loss function, a cross-image unsupervised loss function and an agent-related loss function, and optimizing and updating network parameters of the teacher-student model through the total loss function until the total loss function is minimized, so as to obtain the teacher-student model. Obtaining a trained teacher-student model; and inputting a to-be-segmented scene image into the trained student model, and obtaining and outputting a more accurate image semantic segmentation result.

The invention discloses a global and local multi-scale fused infrared guide low-light image enhancement method, which belongs to the technical field of computer vision, and specifically comprises the following steps: constructing an image enhancement network; constructing a data set, and training the image enhancement network; and acquiring a low-illumination image and a near-infrared image in the same scene, and inputting the low-illumination image and the near-infrared image into the trained image enhancement network to obtain an enhanced image. According to the global and local multi-scale fused infrared guide low-light image enhancement method, the problems that in the prior art, calculation is complex, the effect is single, illumination lifting is not considered, and denoising is difficult to achieve are solved.

The invention discloses a rapid three-dimensional reconstruction method for an unmanned aerial vehicle mine inspection scene, and particularly relates to the technical field of image processing and three-dimensional modeling, and the method comprises four steps: environment perception collection, inclination angle adaptive path planning, image quality optimization and three-dimensional model construction. Sensing information such as illumination, wind speed and gradient through a sensor to dynamically adjust a shooting strategy, and generating a normal supplementary shooting path in a gradient sudden change area; image quality is improved through image enhancement and feature extraction, and a confidence scoring model is constructed to screen high-quality images to participate in modeling; carrying out confidence evaluation on a modeling result, triggering a supplementary shooting mechanism, and improving the precision and integrity of the model; according to the method, the feature extraction stability of the image under the condition of illumination dramatic change is improved, the image coverage integrity of a high and steep slope area is enhanced, and stable control of image acquisition and fusion in a wind disturbance environment is realized, so that the precision, continuity and robustness of three-dimensional modeling are effectively improved.

The invention discloses a financial data intelligent entry and verification method, which comprises the following steps: acquiring an original document image, and processing the original document image by adopting an image enhancement algorithm to obtain an optimized document image; extracting a structured field from the optimized document image, determining field coordinates, and generating a structured data table; performing semantic analysis on the non-standardized text in the structured data table to generate semantic vector representation; identifying missing fields according to the semantic vector representation, querying a historical database, and generating preliminary filling data; comparing the preliminary filling data with historical data through a cross validation mechanism to obtain a validated field value; processing the verified field value by adopting a classification model, and determining a document type; arranging the verified field value according to the document type, and generating a standardized data record; key indexes are extracted from the standardized data records, the index logic relation is verified, and corrected data are obtained.

The invention belongs to the field of hollowing detection, and provides an outer wall hollowing detection method and system, and the method comprises the steps: carrying out the multi-mode detection of an outer wall, and obtaining an infrared thermal imaging image and an ultrasonic detection signal; performing image enhancement processing on the infrared thermal imaging image; respectively carrying out feature extraction on the infrared thermal imaging image and the ultrasonic echo image by using a convolutional neural network; extracting and fusing high-dimensional feature vectors, and aligning an infrared thermal imaging image with an ultrasonic echo image through an image registration algorithm and a spatial transformation model; and classifying the fused feature vectors through a multi-layer sensor according to the data aligned in the step, and outputting the specific position, size and severity of the hollowing area. According to the scheme, the outer wall hollowing detection can be efficiently carried out.

Disclosed is a joint imaging system based on unmanned aerial vehicle platform and an image enhancement fusion method, which are used for realizing thermal infrared oblique photogrammetry and real-time fusion of thermal infrared images and visible light images. The system includes a flying unit as a load platform, a shutter control system for controlling the operation of a load camera, a posture control system for recording the movement track and POS data of the flying unit, an airborne image transmission system for communicating with ground equipment, and an onboard computing unit with an image processing module for receiving the output images and performing image processing and fusion, so as to quickly collect thermal infrared images and visible light images, and improve the recognition capability of feature points in the process of thermal infrared oblique photogrammetry, and ensure the success rate and accuracy of building the three-dimensional temperature field model.

The invention discloses a bridge structure health monitoring data anomaly detection method based on deep learning, particularly relates to the technical field of structure health monitoring, and is used for solving the problems of high environmental interference sensitivity and insufficient cross-modal data fusion capability caused by image enhancement and feature extraction process splitting in the existing method. A cross-domain feature mapping relation is generated through combined training of dynamic image enhancement and a deep learning model, and collaborative optimization of enhancement parameters and feature space is achieved; time-frequency resonance parameters of visual images and acoustic emission signals are fused based on cross-modal convolution, and damage feature space distribution is corrected in combination with an attention mechanism; analyzing and quantifying the structural difference of the cross-domain features by using topology persistence coherence, and iteratively optimizing the feature mapping network through an optimal transmission theory; and finally, a multi-level feature template matching and self-adaptive threshold judgment mechanism is adopted to output an abnormal detection result, so that the robustness and generalization ability of bridge structure health detection in a complex environment are remarkably improved.