356 results about "Histogram equalization" patented technology

The invention discloses a data line surface defect rapid nondestructive detection method based on intelligent image recognition, relates to the technical field of image data processing, and aims to solve the technical problems of difficult defect feature separation and low detection accuracy under complex weaving texture noise interference, and the method comprises the following steps: S1, collecting a data line image and carrying out graying processing; s2, constructing a multi-scale image pyramid, accurately segmenting the image by adopting a self-adaptive threshold algorithm, and realizing rapid detection of surface defects of the data line in combination with Hough transform; s3, frequency domain separation of weaving texture and background noise is realized through Fourier transform, interference is filtered out in combination with a band-pass filtering technology, and then the defect contrast is enhanced through histogram equalization; according to the method, the three-layer Gaussian pyramid is constructed for multi-scale decomposition, and the band-pass filtering technology is combined, so that the weaving texture and the defect signal are effectively separated, the detection accuracy is greatly improved, and the problems of missing detection and misjudgment caused by frequency characteristic confusion are thoroughly solved.

The invention relates to the technical field of image processing, in particular to an anti-collision beam weld defect detection method and system based on image processing, and the method comprises the steps: carrying out the image collection of a weld region of a produced anti-collision beam, and obtaining a gray image; the method comprises the following steps: performing initial partitioning on a grayscale image, respectively obtaining a local complexity index of each initial sub-block, obtaining at least two adaptive sub-blocks based on the local complexity index of each initial sub-block, and performing adaptive local histogram equalization on each adaptive sub-block to obtain a target grayscale image; the method comprises the steps of performing edge detection on a target grayscale image to obtain at least two edge pixel points, performing frequency domain conversion on the target grayscale image according to a gradient direction of each edge pixel point to obtain a frequency domain image, performing filtering processing and time domain conversion on the frequency domain image to obtain a denoised image, and identifying defects in the denoised image by using a neural network. And the defect detection efficiency is improved by inhibiting the periodic texture in the weld seam image.

The present invention relates to the field of rock structure measurement, and disclosed are a three-dimensional pore reconstruction method and system based on a rock image, for use in solving the problem that using a machine learning method to reconstruct pores in a rock image requires relatively high mathematical or computer science expertise, involves high labor and economic costs, and is difficult to apply to small-scale rock pore reconstruction projects. The method comprises: step 1: grouping slice images of each rock sample into one set, and performing preprocessing; step 2: performing grayscale processing, histogram equalization and normalization processing on an image; step 3: using a Harris corner detection algorithm to perform corner detection, sorting Harris response values, selecting key points by setting the number of key points, and using a non-maximum suppression method to screen the selected key points; step 4: segmenting the boundaries of pores in the image by means of an adaptive threshold selection method, and extracting pore features; and step 5: using a marching cubes algorithm to construct a three-dimensional pore reconstruction model. The present invention is used for three-dimensional reconstruction of pores in a rock image.

The invention relates to the technical field of chemical production process monitoring, and provides a chemical storage tank liquid level real-time monitoring method and system based on image recognition, which can automatically generate an accurate illumination threshold value by applying big data analysis and machine learning technologies, can adapt to illumination changes of different chemical scenes, and can realize real-time monitoring of the liquid level of a chemical storage tank. The accuracy of liquid level monitoring under various illumination conditions is greatly improved; for image enhancement and correction, the system adopts a local region adaptive adjustment histogram equalization and optimization CLAHE technology and constructs a feature database assisted threshold segmentation and environmental parameter dynamic adjustment SFS model, so that dark environment images can be enhanced more excellently, strong light reflection can be removed, and reduction liquid level details can be cleared; according to the system, a dynamic weight updating mechanism is created for the first time, the weight is adjusted in real time according to the liquid level change and the image quality, interference of a single image is effectively avoided, and the monitoring reliability is remarkably improved.

The invention relates to the field of image processing, in particular to a pipeline wall defect detection method and system based on image recognition, and the method comprises the steps: obtaining a preprocessed gray-scale map of a pipeline wall; calculating a segmentation demand degree of each pixel point in the grey-scale map, dividing the grey-scale map into a plurality of sub-image blocks by using the segmentation demand degrees, and performing histogram equalization on each sub-image block to obtain an enhanced grey-scale map; and inputting the enhanced grey-scale map into a pre-trained neural network model, and outputting a pipeline wall defect detection result. According to the method, the grey-scale map is dynamically divided according to the segmentation demand degree of the pixel points, and the image can be divided into the sub-image blocks which better conform to the defect features, so that the problem of defect feature loss or misjudgment is avoided, and the defect detection accuracy is improved.

The invention belongs to the technical field of image processing, particularly relates to an image effect enhancement method and system, and aims to improve the image quality through multi-domain collaborative adaptive processing. The method comprises the following steps: firstly, determining an optimal quantization level through content-aware adaptive quantization, generating a quantization image and a region complexity graph, then decomposing the image into smooth, edge and texture sub-bands through multi-scale domain analysis, constructing a characteristic spectrum, then, applying histogram equalization, anisotropic diffusion and nonlinear sharpening to different sub-bands through adaptive characteristic enhancement, and finally, carrying out adaptive characteristic enhancement on the different sub-bands. And residual error optimization is carried out. And finally, multi-domain collaborative fusion is carried out to construct an enhancement matrix, fusion masks and feature correction are applied to obtain a final enhanced image, and the method is suitable for various images, retains details, enhances different regions in a targeted manner, and has a remarkable effect.

The invention relates to the technical field of machine vision, in particular to a precision part size automatic detection method and system based on machine vision, precision part images are collected through a high-precision industrial camera, part positioning is carried out, sub-pixel-level topological feature mapping is carried out on interested area images, and precision part size automatic detection is carried out. Comprising the steps of gray histogram equalization, gray morphological processing, edge detection and edge chain code tracking, construction of an edge point topological feature space, execution of sub-pixel subdivision, obtaining of an edge line through contour analysis of a feature distance and a feature angle, and double-constraint geometric reconstruction based on the edge line. The characteristic distance and the characteristic angle are used for rotation matrix conversion and geometric dimension calculation, a relation model of the geometric dimension and the actual dimension of the part is established, precise part dimension measurement is achieved through dynamic error analysis and compensation, the measurement precision is remarkably improved, the risk caused by unreliability of a single characteristic is effectively reduced, and the measurement accuracy is improved. And the measurement stability is improved.

A data-centric system for analyzing agricultural crops, consisting of: a data acquisition module configured to capture images of agricultural fields using cameras, unmanned aerial vehicles (UAVs) or sensors, with the sensors collecting data on soil moisture, temperature, light, humidity and pH; a data preprocessing module configured to: resize the acquired images to a standardized dimension suitable for input to a deep learning model; apply noise reduction using a Gaussian filter; improve image contrast through histogram equalization; and perform image magnification through rotation, reflection, and scaling transformations; a feature engineering module configured to extract the following features: color features, which include color histograms, mean, and standard deviation of color channels; texture features using Gray-Level Co-occurrence Matrix (GLCM) properties, which include contrast, dissimilarity, homogeneity, energy, angular moment (ASM), and correlation; shape features, which include contour area, perimeter, aspect ratio, and roundness; and other features, which include the green pixel ratio and edge density; a classification module configured to: implement deep learning-based classification models selected from the group consisting of Support Vector Machine (SVM), Artificial Neural Network (ANN), Convolutional Neural Network (CNN), ResNet18, Random Forest (RF), SegNet, VGGNet, Naive Bayes (NBG), Decision Tree (DT), K-Nearest Neighbors (KNN), and DeepLab; detecting and classifying weed species in the images of agricultural fields; and diagnosing plant diseases based on the features extracted from the images of agricultural fields; an output module comprises a user interface configured to display the classification and recognition results; and a recommendation module configured to suggest treatment solutions for diagnosed plant diseases through the output module's user interface.

The invention discloses a photovoltaic module infrared image fault detection method based on unmanned aerial vehicle inspection, which relates to the field of fault detection, and comprises the following steps: configuring an unmanned aerial vehicle platform, planning a flight path, setting aerial photography parameters, setting an infrared thermal image acquisition and infrared image data return and storage mechanism, and performing gray normalization processing, image noise reduction, histogram equalization, edge enhancement processing and size standardization processing on the infrared image data, and performing data enhancement operation. An unmanned aerial vehicle infrared inspection technology is combined with a deep learning target detection model, a set of complete photovoltaic module infrared image fault detection process is established, and full-process automatic processing from image acquisition, image preprocessing, model detection to result evaluation and visualization can be realized. The method has the comprehensive advantages of being high in fault recognition precision, high in detection speed, standardized in processing flow and the like, and the efficiency and the intelligent level of photovoltaic power station component-level fault inspection are remarkably improved.

The invention relates to the technical field of image processing, and provides an image enhancement method and device and electronic equipment, and the method comprises the steps: carrying out the filtering processing of a to-be-enhanced image through employing a multi-scale low-pass filter with all-passband flat response, carrying out the dynamic range compression of the filtered image, and obtaining a multi-scale enhanced image; based on histogram distribution characteristics of the multi-scale enhanced image, dividing a pixel value range of the multi-scale enhanced image into a plurality of sub-intervals, and performing gray boundary truncation processing on each sub-interval to obtain a multi-scale interval image; dynamically determining a window size based on the scale feature of the multi-scale interval image, and performing multi-scale adaptive contrast limit histogram equalization on the multi-scale interval image based on the window size to obtain a multi-scale equalization image; and fusing the multi-scale equalized image to obtain a final enhanced image. According to the image enhancement method and device and the electronic equipment provided by the invention, the image enhancement effect is effectively improved.

The invention discloses an underwater image enhancement method based on color channel unit compensation amount, and is applied to the technical field of underwater image processing. Comprising the following steps: respectively compensating and correcting color cast of attenuated red, green and blue channels of an underwater image by using a color channel compensation method of unit compensation dosage to obtain corrected images of the three channels; carrying out adaptive platform histogram equalization on the corrected image to realize gray scale extension and redistribution of pixel values; for a brightness channel, using a CLAHE algorithm to improve image contrast, using a GUM algorithm to enhance details, and using a Gamma correction algorithm to improve local brightness; and performing multi-scale fusion to obtain a final output enhanced image. According to the method, the quality of the underwater image is remarkably improved, the method has important practical significance in the application fields of underwater target tracking, recognition, positioning and the like, and more reliable visual support can be provided for ocean resource development and scientific research.

The invention discloses a motion blur removing method based on dynamic image interpolation, and the method comprises the steps: firstly correcting a blur track according to the dynamic change of time and direction; then, considering the stage speed change of the target, and carrying out weight updating on the initialized fuzzy kernel; then, under the condition that the target speed cannot be estimated, feature points are detected by using an SIFT algorithm, and the feature points are tracked by using an L-K optical flow method, so that a more accurate dynamic fuzzy kernel is constructed, and then deconvolution operation is performed by combining a neighbor interpolation technology, so that the definition of an original image is recovered; in order to further optimize the image quality, three image enhancement technologies, including histogram equalization, contrast enhancement and image sharpening, are combined to cope with visual interference in different environments, enhance the contrast and detail expressive force of the image, and further improve the image processing effect. Therefore, the unmanned aerial vehicle identification system can capture and identify the hostile target more accurately.

The invention discloses an image enhancement method based on multi-level contrast and dark detail optimization. The method comprises the following steps: carrying out local gray level distribution adjustment on an infrared image; afterwards, dark part details are enhanced through a nonlinear adjustment function, and Gaussian kernels of different scales are adopted to perform multi-scale convolution operation, so that the brightness and detail performance of a dark part region are improved; calculating the global brightness distribution characteristic of the image, and dynamically adjusting the gamma value to change the gray value distribution curve of the image, thereby achieving the comprehensive optimization of the brightness and contrast of the image. And finally, guiding filtering and L0 detail filtering technologies are combined, so that the noise influence is effectively reduced, and meanwhile, the edge features are enhanced. According to the method, noise reduction and edge feature enhancement are carried out on the image based on block histogram equalization, dark region enhancement, adaptive gamma correction, guide filtering and L0 detail filtering, the definition and texture expression of the image are improved, and more reliable visual information support is provided for subsequent infrared target detection and recognition tasks.

The invention discloses a skin image super-resolution conversion system and a skin image super-resolution conversion method based on deep learning, and relates to the technical field of image super-resolution conversion, and the system evaluates whether illumination is qualified or not by collecting skin near-illumination and analyzing illumination conditions of the skin near-illumination, establishing a light environment analysis model and calculating a light influence coefficient. The method comprises the following steps: preprocessing qualified images, including color space conversion, histogram equalization, edge detection and denoising; thirdly, evaluating the quality of image pixels, calculating a plurality of pixel features, judging whether the pixel features conform to the standard or not, and performing optimization through strategies such as sharpening and de-noising; a multi-scale feature extraction network and a self-attention mechanism are adopted to extract image features, a cross-modal mapping model is constructed, super-resolution conversion is performed through a generative adversarial network, and image details and definition are improved. And finally, monitoring the quality of the super-resolution image, evaluating the conversion effect, and adjusting the processing strategy according to the quality coefficient to ensure that the output image meets the medical diagnosis requirement.

The invention provides a defect visual identification detection method for dense welding spots, which comprises the following steps: collecting a high-density welding spot array image, carrying out hierarchical processing on the image by adopting wavelet transform, separating out feature information containing an edge overlapping region and shadow distribution, and obtaining a preliminary welding spot region distribution diagram; according to the preliminary welding spot area distribution diagram, performing feature enhancement processing on edge overlapping areas in the distance between adjacent welding spots by adopting a Laplace operator to obtain an enhanced welding spot boundary image; for the enhanced welding spot boundary image, performing fuzzy processing on shadow boundaries in a distance range of adjacent welding spots, separating contour information of a single welding spot through an adaptive threshold segmentation method, and determining an individual region range of the welding spots; and extracting image features of surface texture, edge integrity and shape regularity of the welding spots from the determined individual region range, and performing normalization processing on gray distribution of each welding spot region by applying a histogram equalization method to obtain corrected welding spot feature data.

The invention discloses a collected image recognition method and system for a surveying and mapping unmanned aerial vehicle, surveying and mapping image data and surveying and mapping point cloud data of a target surveying and mapping area are obtained through a multi-mode sensor carried by the unmanned aerial vehicle, and meanwhile, a time camera is used for capturing a motion signal of a dynamic target to generate event stream data; histogram equalization and multi-scale enhancement are carried out on the surveying and mapping image data, and voxel filtering and curved surface reconstruction are carried out on the surveying and mapping point cloud data; wavelet packet decomposition is utilized to extract frequency domain features of an image in the preprocessed acquisition data, and the frequency domain features are fused with motion trail features in event stream data; and inputting the mixed feature vector into a lightweight adaptive model, dynamically adjusting weights of different modes through an attention mechanism, and outputting a surveying and mapping result of the target surveying and mapping area. The information of the image in different frequencies and directions can be described more comprehensively and meticulously, and the surveying and mapping efficiency is effectively improved.

The invention relates to the technical field of industrial automation quality control, in particular to a bicycle lifting seat production defect detection method and system based on machine vision, and the method comprises the steps: carrying out the illumination decomposition and highlight reconstruction of collected surface data, and generating a balanced texture map with uniform illumination through a gradient domain local repair algorithm and global histogram equalization. Then, the balanced texture map is input into a two-channel parallel analysis architecture comprising a linear flaw attention network and a regional heterogeneity analysis network, and probability maps of scratch defects and oxidation defects are extracted; then, performing spatial correlation intelligent arbitration and weighted fusion based on a local confidence mean value on the two paths of probability graphs so as to eliminate overlapping conflicts and background noise, and generating a fusion defect graph; and finally, carrying out topology and geometric constraint filtering on the fused defect graph, normalizing the defect form, and finally outputting a defect positioning mask representing the position and contour of the defect. According to the invention, identification and distinguishing of surface scratches and oxidation defects of the bicycle lifting seat are realized.

The invention provides a wooden furniture defect detection method based on image recognition, and the method comprises the steps: collecting a furniture surface image and environment information through an industrial camera and an illumination sensor, carrying out the graying, histogram equalization and Gaussian filtering preprocessing, extracting multi-scale features through a convolutional neural network, and carrying out the detection of the defects of the furniture surface image and the environment information; a defect area is positioned in combination with a space attention mechanism, and then the distinction degree of different defect categories is improved through a predefined priority knowledge base and a semantic attention module. And combining space and semantic double attention mechanisms to generate a weight matrix, realizing dynamic redistribution of defect confidence, adding local contrast regularization loss, optimizing response sorting of the model on a multi-defect sample, and finally outputting a defect detection result according to priority and confidence. According to the method, the main defect identification accuracy, the confidence coefficient distribution rationality and the integrity and robustness of overall detection in a multi-defect environment are improved.

The invention discloses a residual capacity balance control method and system for an aged lithium battery, and relates to the technical field of new energy materials and devices, and the method comprises the steps: inputting quantum magnetic field response data and infrasonic wave signals into a single-chip microcomputer, carrying out the wavelet noise reduction and histogram equalization processing based on an STM32 single-chip microcomputer, and generating an acoustic-magnetic coupling feature matrix; inputting a dynamic pheromone equalization model based on the acoustic-magnetic coupling characteristic matrix, calculating a capacity difference coupling coefficient between the battery packs through an ant colony optimization algorithm, and generating a capacity redistribution path topological graph; according to the capacity redistribution path topological graph, the energy transfer direction is controlled through a bidirectional Buck-Boost circuit, and an equalizing current threshold value is dynamically adjusted based on the quantum spin resonance magnetic field gradient; according to the invention, the quantum magnetic field response data of the aged lithium battery pack and the infrasonic wave signal generated by electrolyte decomposition are collected, so that the physical and chemical states in the battery are accurately monitored.

The invention discloses a high-frequency and high-speed circuit board detection method based on infrared image enhancement and mode recognition, and relates to the technical field of circuit board defect detection, and the method comprises the steps: carrying out the non-contact infrared image collection of a high-frequency and high-speed circuit board which is electrified to work through a thermal infrared imager, and obtaining the original infrared image data containing the temperature distribution of each region of the circuit board; performing enhancement processing on the infrared image by applying histogram equalization and filtering denoising algorithms, and highlighting a tiny thermal change region; and extracting heat distribution characteristics from the enhanced infrared image to obtain a heat distribution characteristic sequence table. By combining infrared image enhancement and mode recognition and adopting a non-contact detection mode, physical damage to the circuit board caused by traditional contact probe detection is avoided, the integrity of the circuit board is protected, the service life of the circuit board is prolonged, the defective rate caused by detection is reduced, and the detection efficiency is improved. The method is particularly important for high-precision and high-value products such as high-frequency and high-speed circuit boards.

The invention discloses a deformation monitoring method and system based on a machine vision technology, and relates to the technical field of crossing of structural health monitoring and computer vision, and the method comprises the steps: improving the angular point positioning precision in a calibration stage through a sub-pixel-level Harris angular point detection algorithm; robust recognition and initial positioning of an artificial marker in a first frame of image are realized by using an X-Feat convolutional neural network model, an anti-interference template is generated in combination with histogram equalization and Gaussian filtering, the stability of the system is enhanced, a local search area is delimited by taking an initial positioning point as a center, and a deep learning and interpolation algorithm is combined, so that the anti-interference performance of the system is improved. High-precision tracking and displacement calculation of the marker center are realized, the monitoring resolution is effectively improved, pixel-level displacement is accurately converted into physical space displacement through inverse transformation of a homography matrix, and dual threshold criteria of mode length and change rate are introduced for dynamic early warning, so that the engineering practicability and safety response capability of a monitoring result are improved.

The invention relates to the technical field of image data processing, in particular to a boiler coke accumulation detection system based on unmanned aerial vehicle inspection, the system comprises a processor and a memory, and the processor executes a computer program of the memory to realize the following steps: obtaining at least two correction images and one fusion image of any to-be-detected part in a boiler, if it is detected that a suspected focus area exists in the fused image, a reference area corresponding to the suspected focus area is obtained from each corrected image, and the corrected image is obtained according to the gray value difference of pixel points in the suspected focus area and each reference area, the gray value change features of the pixel points in the suspected focus area and the gray value difference. Obtaining the focus accumulation confidence degree of each suspected focus accumulation area in the fused image; according to the focus accumulation confidence degree of each suspected focus accumulation area, the total weighted frequency number of each gray value in the fused image is obtained, histogram equalization is utilized to carry out enhancement processing on the fused image, an equalized image is obtained, and the accuracy of focus accumulation detection is improved.

The invention provides an industrial visual imaging method and system suitable for a high-humidity water vapor environment and a medium, and the method comprises the steps: obtaining temperature fluctuation data and humidity fluctuation data in the water vapor environment at different time nodes based on a temperature and humidity sensor, and analyzing the environment parameters of a shooting environment; acquiring a shot image, analyzing brightness values of pixel points of the shot image, and performing equalization processing on the brightness values to obtain a brightness average value; analyzing fog concentration information of the shooting environment based on the environment parameters of the shooting environment, and analyzing interference information on the brightness mean value based on the fog concentration information; switching a shooting mode based on the interference information, and carrying out defogging processing on the shot image; carrying out enhancement processing, carrying out number domain stretching on the enhanced image, and then executing histogram equalization processing to obtain a visual imaging image; the shooting mode is adjusted by analyzing the interference information of the fog concentration on the image brightness, the image is defogged, the image is enhanced to recover image details, and the recognition capability is improved.

The invention relates to a pathological section splicing method, system and device and a storage medium, and the method comprises the steps: collecting a pathological section image, converting the pathological section image into a plurality of modes, and carrying out the histogram equalization and denoising processing; calculating an entropy value of each pixel point region in the image, and extracting pixel points with high entropy values as feature points; carrying out preliminary matching on the image under different scales, and carrying out bidirectional matching operation in a matching region to obtain a feature point pair; constructing a deformation model according to the feature point pairs, calculating parameters of the deformation model, and enabling the to-be-registered image to deform based on the deformation model through minimizing deformation energy; gradient information of an image overlapping region is calculated, a weight is allocated to each pixel point in the overlapping region according to a weight allocation rule, and the edge of the image overlapping region is processed to obtain a spliced image; and carrying out quantitative evaluation on the spliced image. According to the method, the problems of low registration precision, large calculation amount and poor adaptability to complex backgrounds in the prior art can be solved.

The invention discloses a crown instance segmentation and maturity evaluation method based on dynamic expansion convolution. The method comprises the following steps: firstly, carrying out radiation correction, geometric correction and histogram equalization preprocessing on a forest RGB image acquired by an unmanned aerial vehicle; then extracting global contour features through an adaptive dynamic expansion convolution module, enhancing local detail capture capability in combination with an edge perception feature pyramid network, and realizing multi-level feature fusion by using a double attention mechanism; then, a density adaptive contour cross suppression algorithm is adopted to optimize the mask, and the distinguishing precision of the dense area is improved through dynamic adjustment of a suppression threshold value and ray method contour cross calculation; and finally, calculating canopy density based on a segmentation result, and constructing a maturity index model by fusing the shape, color and geometric features of the crown breadth. According to the method, the extraction capability of the sub-pixel-level details of the crown edge is remarkably improved, the problems of fuzzy irregular contour segmentation and high-density region misjudgment are effectively solved, and an efficient and accurate technical scheme is provided for forestry resource monitoring.

The invention belongs to the technical field of image processing, and discloses a cable tube well hole site occupation detection method based on multi-view image fusion, which comprises the following steps of: selecting a fixed position in a cable tube well, and acquiring images of the same well wall section from upper, lower, left, right and front five views by using handheld imaging equipment; performing guided filtering denoising and contrast-limited histogram equalization processing on the image to enhance the image quality; extracting a contour and calculating circularity, and determining a candidate circle center through a segmented recursive circle center positioning method; cross-view matching and verification are carried out on the holes with shielding, feature restoration is realized through centroid alignment and curvature similarity calculation, and the hole position state is judged by adopting a majority decision principle; and finally, performing joint decision-making based on the spatial positions, direction consistency and texture features of the cable and the hole, and generating and outputting an occupancy state matrix. The method effectively improves the recognition precision and robustness in a complex shielding environment, and is suitable for power resource management.

The invention relates to the technical field of knowledge reasoning analysis, in particular to a disease and insect pest picture matching case system based on a knowledge graph. The system comprises a disease and insect pest knowledge graph construction module, a to-be-matched visual semantic fusion module, a disease and insect pest case similarity measurement module and a disease and insect pest case prevention and treatment suggestion module, can collect a disease and insect pest knowledge data set corresponding to the agricultural field, extracts hosts and disease and insect pest entities, and constructs a disease and insect pest knowledge graph at the same time. Generating a pest and disease knowledge graph; obtaining a to-be-matched disease and insect pest picture, and performing histogram equalization processing and visual semantic feature fusion to generate a to-be-matched disease and insect pest visual semantic knowledge feature vector; and performing case similarity calculation and case matching sorting processing on the corresponding entity semantic knowledge in the disease and insect pest knowledge graph, and generating a corresponding disease and insect pest case prevention and treatment suggestion scheme so as to execute corresponding disease and insect pest case prevention and treatment suggestion work. According to the invention, accurate case matching of the disease and pest pictures can be realized.

The invention discloses a railway vehicle crack detection method and system based on machine vision, and the method comprises the steps: S1, obtaining continuous images of the surface of a railway vehicle in a motion state, carrying out the motion blur compensation, carrying out the fusion of the compensated images, and obtaining a vehicle surface fusion image; s2, performing adaptive contrast enhancement and improved histogram equalization processing on the vehicle surface fusion image to obtain a processed image; s3, performing crack candidate region positioning on the processed image based on adaptive threshold segmentation and connected domain analysis; s4, utilizing an improved ResNet deep learning network to perform feature extraction on the positioned crack candidate region, and constructing a crack feature vector set; and S5, classifying the crack feature vectors by adopting a multi-scale fusion algorithm based on an attention mechanism, and outputting positions and category results of the cracks. According to the method, the histogram equalization and the learning network are improved to identify the fine cracks and realize accurate positioning and classification.

The invention discloses an unmanned aerial vehicle inspection image acquisition and automatic optimization method and system, and relates to the technical field of unmanned aerial vehicle inspection image automatic optimization, and the method comprises the steps: carrying out the optical correction of an image collected by an unmanned aerial vehicle through a local gray histogram equalization algorithm based on gamma transformation; determining the position and size of the target in the image according to a target detection algorithm; and adjusting the attitude and focal length of the holder according to the position and size of the target. According to the method disclosed by the invention, the problem of overexposure or underexposure caused by the change of illumination conditions is solved through a local gray histogram equalization algorithm based on gamma transformation; through a target detection algorithm, the key target of the power line in the image can be accurately identified, and the attitude of the holder is adjusted according to the position and size of the target, so that the accuracy of defect identification is improved; through a size estimation algorithm and a reverse consistency check mechanism, the focal length of the holder can be automatically adjusted according to the change of the relative distance between the target and the unmanned aerial vehicle, and the image definition is improved.

The invention provides a plastic toy surface defect detection method based on machine vision, and the method comprises the steps: firstly collecting and standardizing a plastic toy surface multi-parameter original image, and carrying out white balance correction and histogram equalization to improve the image quality; secondly, decomposing image frequency domain features by applying multi-scale wavelet transform, and enhancing defect area spatial positioning and texture sensing capabilities by combining cross guidance of frequency domain and spatial domain attention mechanisms; and inputting the fused features into a lightweight convolutional neural network to extract a high-discrimination feature vector, executing defect classification and position regression, and introducing an adaptive mechanism to dynamically optimize a decomposition scale and an attention strategy according to recognition confidence and detection history. The method is suitable for automatic surface quality detection under a complex background.