471 results about "Histogram equalization" patented technology

The invention relates to the technical field of image processing, and discloses an industrial article appearance detection method and system based on machine vision. The method comprises the steps that a workpiece image is collected, and a standardized image is obtained through Gaussian filtering, histogram equalization and Otsu algorithm preprocessing; feature vectors are extracted through edge detection, Hough transform and a gray-level co-occurrence matrix; training a defect identification model by adopting a CNN of an attention mechanism; inputting an image for real-time detection; fusing the detection data and the environmental parameters by using a D-S theory; and establishing a database based on a distributed architecture to generate a quality report. According to the method, the detection accuracy is improved through multi-source information fusion, and efficient management and analysis of mass detection data are realized by adopting a distributed architecture.

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 discloses a high-resolution remote sensing image accurate classification system and method based on deep learning multi-modal fusion, and the method comprises the steps: S1, carrying out the preprocessing and fusion optimization of multi-modal data; S1.1, carrying out the standardization and normalization: carrying out the standardization and normalization of remote sensing data of different modals, and eliminating the influence caused by the difference between the different modals, the difference between the resolution, the spectral range and the like; for optical images, contrast may be enhanced by histogram equalization. The method aims at solving the problems of data heterogeneity, calculation efficiency, over-fitting, difficulty in labeling, real-time performance, interpretability and the like in an existing method by adopting multi-modal data optimization preprocessing, deep fusion model design, automatic labeling and semi-supervised learning, a lightweight model and hardware acceleration technology and a strategy for enhancing interpretability. Through the optimization, the system can maintain high classification precision, improve the calculation efficiency, reduce manual intervention, enhance the transparency and generalization ability of the model, and meet the actual application requirements.

The invention discloses a fire smoke and fire source accurate positioning method based on image recognition, and the method comprises the following steps: S1, collecting multi-modal data, and carrying out the graying, denoising, histogram equalization and shielding correction preprocessing; s2, inputting the preprocessed data into a vortex sensing network to generate a preliminary detection result of flame and smoke; s3, dividing the scene into dynamic space blocks by using a dynamic space division algorithm and allocating priorities; s4, eliminating non-fire feature interference through an environment feature filter, and generating an optimization candidate region; s5, inputting the candidate area into an event-driven time sequence analysis module, and extracting a key frame to analyze flame and smoke diffusion and time trend; s6, verifying position consistency by adopting a flame and smoke joint detection method, and generating a three-dimensional space positioning result; and S7, generating a fire three-dimensional thermodynamic diagram according to three-dimensional positioning and triggering an alarm mechanism. According to the invention, accurate detection and positioning of fire smoke and a fire source are realized by using a vortex sensing network, an event-driven mechanism and the like.

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 invention discloses an oil field power distribution network operation safety violation behavior deep learning recognition system. The system comprises a video acquisition module, a data preprocessing module, an improved YOLO model module, a violation behavior judgment module and a result output module. The video acquisition equipment is distributed in a key area of an oil field site and transmits video data to the server through a wireless network; the data preprocessing module adopts histogram equalization and sharpening operation to enhance the image contrast. The improved YOLO algorithm model is subjected to customized training, and behavior actions of operators and equipment can be accurately recognized. And the illegal behavior judgment module performs matching judgment on the result output by the model in combination with the safety specification of the oil field operation. According to the method, the difficult case mining technology and the multi-stage training strategy are adopted, the judgment capability of the model on illegal behaviors under complex conditions is improved, the OpenCV technology is combined, the capability of the system for coping with light changes and severe weather conditions is enhanced, and the recognition accuracy under different illumination intensities is ensured.

The invention relates to the technical field of image enhancement, in particular to a method for detecting an intestinal lesion area of a child based on machine vision. Carrying out threshold segmentation on the grayscale image to obtain a reference point; clustering the reference points and partitioning the grayscale image according to the shape of a cluster; acquiring edge points and a to-be-optimized area according to the gradient of the pixel points in the block area; obtaining a target point according to the gray difference characteristics of the pixel points and the edge points of the to-be-optimized region; obtaining an abnormal point according to the gray difference characteristics of the target point and other target points in the neighborhood and the gray difference characteristics of the neighborhood of the edge points; and obtaining a gray adjustment range value according to the gray difference characteristics of the neighborhoods of the abnormal points and the edge points in the to-be-optimized region. According to the method, a final gray value is obtained according to a gray adjustment range value and a gray discrete feature of an edge point neighborhood; histogram equalization is carried out according to the final gray value, an intestinal tract detection enhanced image is obtained, and the image enhancement accuracy is improved.

The invention discloses a chip defect visual detection method, and relates to the technical field of chip defect detection.According to the chip defect visual detection method, real-time correction of optical path deviation is achieved in the image acquisition stage through a multi-angle shooting strategy and a self-adaptive optical adjustment mechanism, and comprehensiveness and consistency of image data are enhanced; meanwhile, a deep denoising network based on a convolutional auto-encoder CAE is introduced, feature distribution of a noise-free image is learned, random noise in the collected image is specifically removed, important detail features of a welding spot region are reserved, a low-contrast region is optimized in combination with an adaptive enhancement technology and histogram equalization in an enhancement stage, and a high-resolution image is obtained. The edge characteristic of the welding spot is more prominent, and the visual definition and the characteristic visibility of the image are remarkably improved; in the feature extraction stage, a multi-scale convolutional network is adopted to capture the edge change of a welding spot crack and the texture characteristics of material degradation, and meanwhile, the detail change of a potential defect area is focused through an attention mechanism, so that the accuracy and efficiency of feature extraction are improved.

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 computer vision and artificial intelligence, and provides a method and system for monitoring behaviors in an elevator. The method comprises the steps that an original frame image of an elevator is input into a target detection model, a target detection result is obtained, the target detection result comprises a target detection frame, a region of interest is determined based on the detection frame, the region of interest is input into a resolution reconstruction model, a high-resolution image is output, and the high-resolution image is the region of interest with the enhanced resolution. Fusing the region of interest with enhanced resolution with the original frame image to obtain a fused image, performing histogram equalization on the fused image to generate a target frame image, inputting the target frame image into a behavior recognition model to obtain a behavior recognition result, and obtaining a behavior monitoring result according to a target detection result and the behavior recognition result. Through automatic target detection and behavior recognition, an accurate, efficient and robust elevator monitoring solution can be provided.

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.

The invention discloses a crack identification method and device based on multi-scale infrared thermal feature fusion, and the method comprises the steps: obtaining the gray images of cracks and noise for a single-frame infrared thermal image; histogram equalization is adopted to adjust image gray level distribution, and a gray level enhanced image is obtained; setting a larger local feature sensitivity weight coefficient for the background dominant window and the noise dominant window, and setting a smaller local feature sensitivity weight coefficient for the target composite window; counting the number of foreground pixels in each window, and calculating the ratio of the number of foreground pixels to the total number of pixels; determining a background dominant window, a noise dominant window and a target composite window; setting local feature sensitivity weight coefficients of a background dominant window, a noise dominant window and a target composite window; segmenting the grayscale enhanced image to obtain a binary image, and extracting a connected domain of foreground pixels; for each foreground pixel connected domain, calculating the total number of pixels, randomly selecting a plurality of seed points, determining corresponding maximum inscribed circles, taking the average value of the diameters of all the maximum inscribed circles as the width, taking the ratio of the total number of pixels to the width as the length, and screening out connected domains with the length-width ratio smaller than 10 as noise; and optimizing the foreground pixel connected domain, removing fine noise, smoothing the contour, and regarding the finally obtained foreground pixel connected domain as a crack.

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 provides an infrared image enhancement method based on CLAHE. The infrared image enhancement method comprises the following steps: step 1, obtaining an original image I; 2, processing the original image I to obtain a Gaussian smooth layer and a Gaussian detail layer; step 3, performing histogram statistics on the Gaussian smooth layer, correcting a histogram to retain an effective gray level, and performing histogram mapping; step 4, performing gamma correction on the mapped image; step 5, carrying out four-block processing on the image after gamma correction, then carrying out statistics on a histogram of each block, and then correcting each histogram; step 6, carrying out platform histogram equalization on the Gaussian detail layer to obtain a detail enhancement graph; step 7, adding the detail enhancement image and the dimming smooth image to obtain # imgabs0 #, and performing anti-overflow processing on the # imgabs1 #; according to the infrared image enhancement method based on the CLAHE, the comfort level of human eyes is improved, the contrast ratio of an image area is improved, image details are enhanced, and the image sharpness is improved.

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 relates to a system and a method for making a colored low-light image. The system for making the color low-light-level image comprises a high-sensitivity CMOS imaging module which is used for enhancing the photon collection capability and reducing the noise level to complete imaging; the image enhancement processing module adopts a histogram equalization algorithm and comprises the steps of calculating a gray histogram of the original image, calculating a cumulative distribution function according to the gray histogram and performing gray mapping to obtain an enhanced gray value; the image noise reduction processing module is used for carrying out noise reduction on the image after enhancement processing, and filtering is carried out by adopting a night image noise reduction technology, a bilateral filtering method including spatial domain noise reduction and a band-pass filter including frequency domain noise reduction; and the colorful low-light-level image generation module obtains a colorful low-light-level night vision image by adopting an interpolation algorithm and makes a colorful low-light-level image. According to the system and the method for manufacturing the colored low-light-level image, special equipment is not depended on, the imaging quality of the colored low-light-level image can be improved, and the colored low-light-level image can be manufactured.

The invention discloses an ultra-low illumination tobacco leaf image enhancement method and system based on multi-scale fusion, and belongs to the technical field of tobacco leaf image enhancement. The method comprises the following steps: S1, acquiring extremely low illumination tobacco leaf image data; s2, splitting the colored ultra-low illumination tobacco leaf image into R, G and B color channels; s3, enabling the fuzzy region to be clearer through a histogram equalization method, and obtaining a first image; s4, adapting to different illumination conditions by using a dual Gamma correction algorithm, and improving visual consistency to obtain a second image; s5, using an improved CLAHE algorithm to retain channels and collaterals of the flue-curing barn tobacco leaf image, and improving the image quality to obtain a third image; and S6, using a detail enhancement algorithm to improve specific area details of the curing barn tobacco leaf image, and retaining detail features to finally obtain a curing barn tobacco leaf curing enhancement image. Through the combination of various algorithms, the tobacco leaf image can be effectively enhanced even under the extremely low illumination conditions of serious degradation such as low signal-to-noise ratio, detail information loss and color distortion.

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 relates to the technical field of image shooting and processing, and discloses an anti-overexposure image processing method and system under strong light based on a high dynamic range, and the method comprises the steps: collecting a plurality of frames with different exposures for the same scene through an extremely weak light full-color night vision device, carrying out the multi-frame fusion, and generating a high dynamic range image; the method comprises the following steps: mapping an image with a high dynamic range to display equipment with a low dynamic range, and readjusting the brightness and contrast of the image to realize tone mapping; different areas in the image are independently processed, an overexposure area is identified, the brightness of the overexposure area is independently reduced, and the original brightness and contrast of other areas are kept; histogram equalization parameters are dynamically adjusted according to the local brightness distribution of the image, the image contrast and details are improved, and image color correction is achieved. The problem of overexposure of the ultra-weak light full-color night vision device in a strong light environment is solved through a high dynamic range algorithm, and the image quality of the night vision device under different illumination conditions is remarkably improved.