2522 results about "Histogram" patented technology

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

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

The invention provides an image-based three-dimensional point cloud traffic marker classification method and system, and relates to the technical field of computer vision, and the method comprises the steps: collecting a multi-frame multi-angle image of a traffic marker, converting the multi-frame multi-angle image into point cloud data, constructing a histogram based on reflection intensity and geometric features, determining an optimal segmentation threshold value, and carrying out the segmentation of the point cloud data; the method comprises the following steps: carrying out region merging by combining spatial distribution characteristics of scattering coefficients and absorption coefficients to obtain point cloud sub-regions with uniform materials, determining a core region by utilizing a curvature entropy value and a normal vector entropy value, establishing a local coordinate system, carrying out region growth and dynamic merging through spatial position and geometric structure constraints to obtain candidate point cloud sub-regions, and carrying out point cloud distribution on the candidate point cloud sub-regions. And extracting the position offset and the attitude variation to construct a time sequence motion feature, thereby realizing accurate classification of the traffic markers.

The invention discloses a video stream dynamic fragmentation encryption and block chain evidence storage method, and relates to the technical field of video content security, and the method comprises the steps: calculating a color histogram difference value and an optical flow vector change rate between adjacent frames of an input video, marking the difference value as a scene switching point when the difference value exceeds a preset threshold value, and storing the scene switching point; the method comprises the following steps: preliminarily dividing a video into a plurality of scene segments according to scene switching points, performing content complexity evaluation on the scene segments, calculating gray level co-occurrence matrix characteristics of each frame of image through texture density analysis, calculating edge complexity to extract the number and distribution of Canny edges, and performing motion vector statistics to analyze the size and direction of inter-frame object displacement. The change rate between adjacent pixels in the color space is measured according to the color change gradient; the video stream dynamic fragment encryption and block chain evidence storage method is suitable for video contents of different types and complexities, and has relatively high detection accuracy and robustness.

The invention relates to the technical field of machine learning, in particular to a grain classification and recognition method and system based on image analysis, and the method comprises the following steps: obtaining grain particle image data, calculating a gray histogram and a gradient change rate, calling a gray co-occurrence matrix to extract grain particle texture density, and scanning a sliding window to obtain local feature parameters of grain particles. According to the method, through combined analysis of a gray level histogram and a gradient change rate, dynamic extraction of particle textures through a sliding window, enhancement of complex surface feature capture, normalization of contrast and direction consistency parameters, construction of weighted feature vectors, dynamic correction of variance contribution degree and suppression of environmental interference, and fusion of near-infrared and short-wave infrared reflectivity changes, the near-infrared and short-wave infrared reflectance change is improved. And quantifying the mean value and peak-to-valley ratio of spectrum difference values, matching visible light characteristics to verify multi-dimensional constraints, constructing a spectrum-texture matrix by local reflectivity rate and gradient change rate, and synchronously evaluating global similarity and spatial distribution difference by adopting Euclidean distance and local offset double-layer matching.

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

The invention discloses a BIM model automatic generation method and system based on point cloud data, and belongs to the field of building information modelling, and the transmission method comprises the steps: obtaining original point cloud data, and employing a filtering algorithm based on point cloud density adaptive adjustment to carry out the preprocessing of the point cloud data; constructing a voxel octree structure for the preprocessed point cloud data, and performing semantic classification on the point cloud; geometric modeling is carried out based on the segmented point cloud subsets, and a fitting algorithm is adopted to carry out shape completion on a point cloud area; semantic annotation is carried out on the components subjected to geometric reconstruction, a corresponding relation between component types and spatial attributes is constructed, fusion features based on a point feature histogram and a local curvature are adopted, and classification is carried out; a standard BIM component family is converted, and a three-dimensional BIM model is constructed through the mapping relation. According to the method, the voxel octree data structure and the deep semantic segmentation neural network model are combined, division and semantic recognition are performed on the point cloud data, the intelligent degree of the model is improved, and manual intervention is reduced.

The invention relates to a hydraulic engineering concrete crack intelligent identification and quantitative analysis method and system based on machine vision. The method comprises the following steps: firstly, acquiring an original image sequence of the surface of a hydraulic engineering concrete structure, classifying according to illumination intensity, shooting angle and shooting distance, extracting crack edge features through a convolutional neural network, and fusing to obtain a crack feature set; correcting illumination through adaptive histogram equalization, correcting angles and distances through geometric transformation, and combining edge detection and scale invariant feature transformation to obtain standardized geometric parameters including crack length, maximum width and the like; if the parameter exceeds the engineering safety standard threshold value, tracking a crack track through an optical flow method to calculate increment, and inputting a neural network to output a damage trend; and finally, generating a three-color risk distribution diagram by using a finite element based on the trend, extracting high-risk data to calculate a real-time evaluation value, and dynamically adjusting the monitoring frequency to generate an optimization strategy. By adopting the method, the reliability and economy of engineering safety monitoring can be remarkably improved.

The invention relates to the technical field of image enhancement, in particular to a protector bimetallic strip detection method based on machine vision, and the method comprises the steps: obtaining a surface image of a bimetallic strip, and carrying out the preprocessing of the surface image, and obtaining a gray image; performing threshold segmentation on the grayscale image to obtain at least one segmentation region; obtaining an evaluation coefficient of each segmented region, obtaining an adaptive cutting parameter when contrast-limited adaptive histogram equalization is carried out on each segmented region according to the evaluation coefficient of each segmented region, and carrying out image enhancement on each segmented region in the grayscale image according to the adaptive cutting parameter to obtain an enhanced grayscale image; the corrosion detection result in the bimetallic strip is obtained by using the enhanced gray level image, so that the enhanced image can reflect more effective information, and the surface corrosion detection precision of the bimetallic strip according to the enhanced image is improved.

The invention relates to the technical field of gesture recognition, in particular to an anti-interference optimized gesture recognition method. Comprising the following steps: acquiring a gesture video stream through a camera, constructing a dynamic background model by using a frame difference method and a Gaussian mixture model, eliminating a static background and interference, and extracting a target area image; performing local brightness histogram analysis on the target region image, and optimizing the image quality by adopting a region adaptive compensation algorithm and a multi-scale edge enhancement technology; positioning a gesture area in real time by using a color histogram and a feature matching algorithm, and dynamically updating a gesture track in combination with Kalman filtering; and extracting gesture shapes, tracks and dynamic mode features through deep learning, comparing the features with a standard model library, and outputting gesture categories and corresponding function instructions. According to the method, a multi-level optimization strategy is adopted for a complex background, a dynamic target and a changeable illumination environment, so that the anti-interference capability and the recognition precision of gesture recognition are improved.

The invention discloses a mobile terminal streetscape image real-time segmentation method based on a lightweight neural network, and relates to the technical field of image segmentation. The method comprises the following steps: firstly, carrying out 320 * 320 adjustment, Z-score standardization, adaptive histogram equalization and 3 * 3 Gaussian filtering preprocessing on an input streetscape image; then, an improved MobileNetV3 backbone network is used, and a five-scale feature map is output in combination with DropBlock regularization through eight feature extraction stages including depth separable convolution and an SE attention module; multi-scale features are fused through a U-shaped structure, and a fusion feature map is generated through up-sampling, element-by-element addition of dimension reduction low-layer features and an attention gating module; and during reasoning, outputting a segmentation mask by using a convolutional layer, Softmax and a conditional random field, and finally performing knowledge distillation, weight pruning, 8-bit quantization and TensorRT optimization. According to the invention, high-precision real-time street view segmentation is realized, the robustness is high, and the method is suitable for different devices and scenes.

The invention relates to a segmentation model based on a CNN and Transform parallel double-branch structure, and belongs to the technical field of medical data prediction. The method comprises the following steps: acquiring a CT image, and preprocessing the CT image by executing windowing processing and contrast limited adaptive histogram equalization; extracting features of lung lesions in the preprocessed CT image through a parallel double-branch structure; inputting the extracted features into a cross enhancement fusion module, and performing complementary fusion on the features through dynamic weight distribution to obtain fused features; the fusion features are input into a multi-scale context information extraction module, and lesion boundary sensitivity is enhanced through cavity convolution of different expansion rates; the encoder features and the decoder features are fused through jump connection, and a segmentation result is output after resolution is recovered based on up-sampling; and optimizing model training by adopting a weighted loss function. Accurate segmentation of the lung lesion in the pulmonary tuberculosis CT image is realized, and clearer and more accurate lesion area information can be provided.

The invention belongs to the technical field of face recognition, and particularly relates to a face recognition algorithm adaptive to illumination variation, which uses Gaussian filtering to reduce noise caused by illumination variation, convert a color image into a grey-scale image, reduce calculation complexity, perform adaptive histogram equalization, estimate global illumination conditions in the image and improve the face recognition accuracy. Calculating the main direction and intensity of illumination in the image, predicting the current illumination condition, and enabling the image brightness to adapt to different environment illumination; identifying a face region in the image, using a deep learning method to position face related points, using an LBP to extract local features insensitive to illumination variation, and using a deep convolutional neural network to extract global features; introducing a multi-illumination data set; the Euclidean distance is used for comparing the extracted feature vectors, the similarity between the extracted feature vectors and known faces in a database is recognized, according to the similarity score, threshold judgment is adopted for AQW to obtain a final recognition decision, and the method has the effect of being capable of accurately adapting to facial features under different illumination conditions in real time.

The invention relates to the technical field of data asset exploration, in particular to an intelligent data asset exploration method and system based on a knowledge graph, and the method comprises the steps: obtaining enterprise data assets, carrying out the dynamic annotation, and generating an annotation data set; performing semantic analysis based on the annotation data set to obtain a semantic association graph; constructing a knowledge graph model, calculating node weights, association path lengths and path contribution degrees, and generating a simulation association distribution graph; segmenting the semantic association graph through the node density and the relationship strength to obtain an actual association distribution graph; and adjusting the knowledge graph model based on the actual and simulated association distribution histogram. According to the invention, the intelligent level and accuracy of data asset exploration can be improved.

The invention discloses a carbon-coated foil coating uniformity detection method and system based on machine vision, and the method comprises the steps: S1, collecting blue light, green light, red light and near infrared spectrum images of the surface of a carbon-coated foil coating through a multi-angle camera, and generating a fused composite image through a multispectral image fusion algorithm; s2, performing adaptive histogram equalization and reflected light spot suppression processing on the fused composite image, and outputting an enhanced image; s3, coating edge features in the enhanced image are extracted, segmentation of a coating region is realized in combination with a region growing method, and a coating region mask is obtained; s4, extracting multi-scale texture features of the coating region by adopting a pre-trained ResNet-50 deep convolutional network, and constructing a coating surface feature vector; and S5, based on the extracted feature vectors and a standard template library, through a uniformity evaluation model, calculating a uniformity index of the coating, and detecting the uniformity of the coating. According to the invention, accurate detection of the coating uniformity can be realized.

The invention discloses a corn germination image segmentation method based on an elite adaptive rime algorithm. Relates to the technical field of agricultural seed detection and image processing, in particular to the technical field of corn germination image segmentation based on an elite adaptive rime algorithm. According to the method, a dual-adaptive weight mechanism and an elite reselection strategy are introduced into a rime optimization algorithm, the convergence capability of the algorithm is enhanced, and multi-threshold segmentation is carried out on the corn kernel germination image in combination with the Kapur entropy. And the image segmentation precision is effectively improved. The method comprises the following steps: acquiring a corn germination image data set; drawing a two-dimensional histogram, and inputting the two-dimensional histogram into a Kapur entropy function to obtain an objective function fobj; an elite solution module is initialized; a dual-adaptive weight mechanism is added; a soft rime strategy and a hard rime strategy are improved; updating the elite solution after the rime search strategy module; and the target function fobj is input into a rime improvement algorithm, and an optimal threshold value is obtained.

The invention relates to the technical field of image processing, and discloses an intelligent weight monitoring method, system and device for bred pigs and a storage medium. The method comprises the following steps: acquiring an original data set through multi-angle image acquisition; carrying out image processing by utilizing adaptive histogram equalization and Gaussian filtering; obtaining body shape parameters by adopting contour-skeleton feature extraction and curvature analysis; performing multi-source fusion processing in combination with the data of the Internet of Things; body weight modeling is carried out based on a hierarchy-increment prediction model and an attention mechanism; and generating a feeding scheme through reinforcement learning. According to the invention, the technical problems of insufficient data fusion and low prediction precision in the existing breeding pig weight monitoring method are solved.

Systems and Methods are disclosed for detecting acute coronary syndrome (ACS) events, arrythmias, heart rate abnormalities, medication problems such as non-compliance or ineffective amount or type of medication, and demand / supply related cardiac ischemia. The system may have both implanted and external components that communicate with a Physicians's programmer, and smart-devices for monitoring and alerting to detected medically relevant events or states. At least one processor provides event detection using statistical threshold criteria calculated upon at least a portion of a patient's data / distributions and set for a patient or based upon what a doctor determines as abnormal for a patient. Cardiovascular condition is tracked using histogram, trend, and summary information related to heart rate and / or cardiac features such as S-T segment measures of heartbeats. Heartbeats with elevated rates, and below a “high” range, provide medically relevant detections including medication non-compliance. Novel methods of power management and patient monitoring are disclosed.

The invention discloses a cultural relic digital fingerprint identification method based on geometric texture and spectral information, and the method comprises the steps: obtaining multi-source data, carrying out the texture mapping, and forming a three-dimensional grid model with texture data; curvature values and topological features of all vertexes of the three-dimensional grid model are calculated, feature fusion is carried out on the basis of local saliency adjustment feature weights, and fingerprint point location screening is completed; based on the screened fingerprint point locations, a significant plane is constructed through a bit plane, and texture features of the cultural relics are extracted based on an improved histogram and a gray-level co-occurrence matrix; extracting characteristic wave bands of the cultural relics based on the screened fingerprint point locations in combination with the hyperspectral data; and multi-modal fusion identification is carried out based on features of geometry, texture and spectral information. According to the method, the geometric structure, the texture features and the hyperspectral information are fused, multi-mode collaborative cultural relic identity high-precision identification is achieved, and the method has the advantages of being high in accuracy, high in robustness, good in interpretability and capable of adapting to complex environments.

The invention discloses an infrared image enhancement method based on scene segmentation. The method comprises the following steps: (1) inputting an original infrared image into an infrared image scene segmentation network based on edge feature guidance to obtain a scene soft segmentation result; (2) decomposing the infrared image into a background layer image and a detail layer image by adopting bilateral filtering; (3) performing adaptive enhancement on the detail layer image based on characteristics of different scenes; (4) enhancing the background layer image by adopting contrast limited adaptive histogram equalization; and (5) carrying out weighted fusion on the enhanced detail layer image and the background layer image. According to the infrared image enhancement method based on scene segmentation, the semantic segmentation algorithm is introduced, the infrared image is divided into different scene areas, and the adaptive infrared image detail enhancement parameters are set for the detail information and noise features of each scene area, so that the quality of the infrared image can be effectively improved.

The invention relates to the technical field of liquid crystal display, in particular to a liquid crystal display driving control method and system. Comprising the following steps: SS01, multi-modal sensing and state triggering: fusing a user distance, a fixation point coordinate and ambient light illumination in real time by using an integrated sensor, and triggering an eye protection mode or a self-adaptive sleep mechanism according to the user distance, the fixation point coordinate and the ambient light illumination, SS02, image partitioning and strategy generation: dividing a screen into N * M partitions, and SS03, distinguishing a dynamic region from a static region according to the brightness histogram and the motion vector of each partition, and outputting differentiated refresh rates and backlight strategies, and SS03, performing big data learning and model optimization, and collecting user behaviors. The system has the advantages that the infrared proximity sensor, the eyeball tracking sensor, the distance sensor and the ambient light sensor are integrated through the multi-mode sensing and state triggering module, multi-dimensional data such as user distance, fixation point coordinates and ambient light illuminance are fused in real time, and active intelligent sensing of the user state and the environment is achieved.

A method comprising: receiving an image block unit of a frame, the image block unit comprising samples in color channels comprising at least one chrominance channel and one luminance channel; reconstructing samples of said luminance channel of the image block unit; determining a reference area for predicting target samples of at least one color channel of the image block unit, wherein said reference area comprises one or more reference samples in a neighboring block in current color channel / frame, in the neighboring of a co-located block in reference color channel / frame; and / or inside the co-located block in reference color channel / frame; determining weights for predicting said target samples based on a ratio between a normalized luminance histogram in said reference area and a normalized luminance histogram co-locating said target samples; determining filter coefficients of a filter for said predicting based on the weights, the reference samples and a shape of the filter; and predicting samples of at least one color channel of the image block unit based on the samples of said luminance channel and the filter coefficients.

The invention provides a coal impact tendency evaluation method which comprises the following steps: sampling a coal rock material on an engineering site, and processing the sample into a coal rock test piece with a preset size and a preset shape; carrying out a uniaxial compression experiment on the coal rock test piece, and recording a motion image of an ejection body on the coal rock test piece in the loading process of the coal rock test piece; comparing cosine similarities among the plurality of images in the moving images, and determining the image with the maximum damage degree according to the cosine similarities; analyzing gray level distribution of the image with the maximum damage degree to obtain a gray level histogram; identifying particle information of the broken particles in the image with the maximum damage degree; representing the damage degree of the coal rock material by using the particle information of the crushed particles; and evaluating the coal impact tendency by using the damage degree of the coal rock material. According to the evaluation method for the coal impact tendency disclosed by the invention, a new coal impact tendency evaluation index is provided, the research on the damage process of the coal rock material is perfected, and basic parameters are provided for subsequent early warning of damage of the coal rock material.

The invention belongs to the technical field of smart cities, and discloses a point cloud-based urban building roof wireframe reconstruction method, which comprises the following steps of: fusing a fast point feature histogram and a multi-scale roof geometric descriptor, generating robust point-by-point features, screening candidate angular point clusters in combination with a classification head, and adaptively segmenting the candidate point clusters based on density parameters by utilizing DBSCAN (Density-Based Spatial Clustering of Applications with Noise), so as to reconstruct a point cloud-based urban building roof wireframe. Initial inflection points are extracted through unsupervised clustering, noise is effectively suppressed, and irregular distribution is adapted; then multi-scale geometric information of an initial inflection point neighborhood is aggregated through an inflection point correction network, offset is learned to correct position deviation, and inflection point positioning precision is improved; and finally, the edge classification network automatically deduces the topological connection of the roof wireframe based on the geometrical relationship and feature relevance of prediction inflection points, so that error accumulation caused by dependence on manual rules in a traditional method is avoided, the generalization ability of a complex roof structure is enhanced, the correction network learns offset through a multi-scale context, and the inflection point positioning precision is remarkably improved.

The invention provides a microscopic automatic focusing method and system based on image gray histogram characteristics, and the method comprises the steps: collecting an image sequence under different focal lengths, carrying out the fuzzy processing, extracting a gray histogram of each frame of image, and calculating the peak position and full width at half maximum of the histogram as the evaluation characteristics of the image definition; calculating the variance of each feature and automatically allocating a weight according to the relative response degree; and finally, comprehensively evaluating the image definition through a weighted definition scoring function, and selecting the focal length corresponding to the image with the optimal score as the optimal focusing position. The method is based on the global features of the gray histogram, is high in anti-noise capability, is adaptive to different imaging scenes through weight adaptive adjustment, is low in calculation complexity, supports real-time focusing, is especially suitable for high-noise fluorescence microscopic imaging scenes, is high in system portability, is low in operation threshold, and effectively improves the accuracy and stability of microscopic automatic focusing.

An adaptive dependency replay control system for use in a latency-sensitive ad serving backend, comprising: a microcontroller-based replay control engine configured to receive ad serving requests and interface with a variety of downstream microservices; a latency monitoring unit operatively coupled to the microcontroller, the latency monitoring unit continuously sampling and maintaining real-time latency histograms for each downstream microservice over sliding time windows; a health status aggregator communicatively coupled to the retry control engine, the aggregator configured to receive service-level health indicators, including, but not limited to, HTTP status codes, circuit breaker states, request timeout counters, and error rate thresholds; a retry decision processing unit stored in a memory accessible to the microcontroller, wherein the matrix can generate a retry action vector based on one or more of the following factors: dependency health, request priority, estimated ad impression value, and system resource metrics; a policy execution engine configured to evaluate retry policies expressed in a domain-specific retry policy language, wherein the execution engine resolves the policies into bytecode rules that are executed by the retry control engine in real time on a per-request basis; and at least one fallback path generator capable of returning an approximate or synthetic response instead of retrying a degraded dependency, where the fallback path is selected based on runtime evaluation of the policy conditions and a calculated retry confidence value.

The invention belongs to the technical field of short video abnormity review, and discloses a short video analysis processing method and system based on AI intelligence, and a storage medium, the complexity entropy value and the motion vector change rate of a target short video are analyzed, and then a logic is triggered to execute a dynamic fragmentation operation based on a preset adaptive fragmentation mechanism, so that the short video abnormity review efficiency is improved. The fragmentation strategy can be dynamically adjusted according to the real-time change of the video content, and the universality and adaptability of fragmentation processing are improved. According to the method, the candidate key frames are identified by acquiring the HSV histogram difference degree and the SIFT feature matching degree of each frame of image corresponding to each video clip, and the candidate key frames are screened based on the preset key frame selection redundancy prevention mechanism to obtain each key frame, so that a key frame set is ensured to be simple and efficient, the efficiency and quality of each link of video processing are improved, and the user experience is improved. And the accuracy of subsequent data analysis is ensured.

The invention belongs to the technical field of image processing, and particularly relates to a monitoring image defogging enhancement method in a high-dust environment of a mineral separation site, which comprises the following steps of: performing smooth denoising on an original image by using weighted guided filtering, and obtaining global atmospheric light through a quadtree subdivision method; constructing a same-color heterogeneous discrimination index combining a spectrum similarity factor and a texture confidence factor for distinguishing dust and ore with similar colors; calculating a pixel-level dynamic defogging coefficient based on the discriminant index, and obtaining an adaptive transmissivity in combination with dark channel prior; and finally, restoring the image by using an atmospheric scattering model and carrying out contrast-limited adaptive histogram equalization processing. According to the method, the problem of misjudgment caused by the fact that the colors of the ore and the dust on the ore dressing site are similar is effectively solved, powerful defogging of the dust area and detail reservation of the ore area are achieved, and the definition and the contrast ratio of the monitoring image are improved.

The invention relates to the technical field of deep learning and computer vision, and discloses a histogram interactive attention-based pavement crack segmentation network processing method and system, so as to enhance the edge detail fidelity and improve the crack segmentation precision. The method comprises the steps of image preprocessing, up-sampling, down-sampling, feature fusion and image reconstruction processing. Wherein global feature modeling in the intensity sub-boxes and among the sub-boxes is realized by constructing a histogram interactive attention module (HIA); a double-branch detail enhancement feedforward module (DDEF) is introduced to enhance spatial detail and high-frequency edge information expression; meanwhile, a Fourier jump enhancement module (FFSM) is adopted to jointly refine jump connection features in a spatial domain and a frequency domain. Through the synergistic effect of the modules, the network can realize continuous recovery and structural consistency modeling of a crack boundary in a complex pavement environment, so that the accuracy and the stability of a segmentation result are remarkably improved.

The invention relates to the technical field of underwater image enhancement, in particular to an underwater image enhancement method based on balance, correction and deblurring and application. The balance module is cascaded by color balance and illumination balance, and the color balance performs compensation balance on RGB (Red Green Blue) color channels of the underwater image based on a white balance algorithm of red channel compensation; according to illumination balance, an improved contrast-limited adaptive histogram enhancement method is used for enhancing illumination of a low-illumination area of an underwater image, and meanwhile illumination of a high-illumination area is restrained, so that illumination balance is achieved. In the correction module, a color and illumination composite correction method combining color constancy and an illumination diagram is provided so as to correct the color and illumination of the underwater image at the same time. And in the deblurring module, the image is deblurred by adopting a multi-scale anti-sharpening mask, so that the definition of the underwater image is improved.