800 results about "Textural feature" patented technology

The invention relates to a medical image segmentation method and system based on residual Mama and multi-scale boundary enhancement. The method comprises the following steps: acquiring and preprocessing a medical image; inputting the image into a segmentation model based on an encoder-decoder architecture; the encoder synchronously extracts local texture features and models long-range spatial dependence through residual error convolution blocks and residual error Mama blocks which are alternately connected; fusing and enhancing the jump connection features between the encoder and the decoder through a boundary enhancement module to optimize boundary characterization; integrating a multi-scale gating attention module in a decoding path, and adaptively selecting and fusing multi-scale context features; and finally outputting the high-precision segmentation mask. The method effectively solves the problems that in the prior art, long-range dependence and local details are difficult to consider, the multi-scale feature fusion capability is insufficient, boundary segmentation is fuzzy and the like, and the segmentation accuracy, the boundary continuity and the clinical practicability are remarkably improved.

The invention relates to the field of defect detection, in particular to a wallpaper defect detection method and system based on machine vision. The method comprises the following steps: analyzing acquired to-be-detected wallpaper image data to obtain regional texture variance, edge density and brightness gradient indexes, and generating a scale image layer set; extracting salient edge points in each scale image and generating an edge anchor point set; analyzing the scale image layer set and the edge anchor point set to obtain a texture difference index, and generating a texture feature map set according to the edge anchor point set; processing the texture feature map set to generate a texture significance distribution map; based on the texture significance distribution diagram, main direction distribution is extracted, a direction residual error model is constructed, and a residual error diagram and a direction difference distribution diagram are generated; and establishing a region scoring matrix, and generating a wallpaper defect credibility distribution map and a defect classification image output set on the basis of the region scoring matrix. The wallpaper defect detection precision can be improved.

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

The invention provides an AGV navigation texture feature extraction and identification method, which comprises the following steps: analyzing the spatial distribution position of a plurality of extracted matching peak values according to the matching peak values, counting the number of the peak values and the repeated spacing between the peak values, and identifying a positioning fuzzy region caused by texture cycle repetition; scanning a local aperiodic mark position according to the identified positioning fuzzy region, and performing uniqueness verification based on the geometric shape feature and color attribute of the mark to obtain a unique anchor point coordinate and a mark type; according to the unique anchor point coordinate, combining with the tile texture data to extract a texture repetition interval, analyzing the size of the texture repetition interval and a preset threshold value, and determining a periodic intensity value; dynamically adjusting a position screening range according to the periodic intensity value, establishing a screening constraint condition, and applying the screening constraint condition to a phase correlation method to obtain a preliminary positioning candidate point set;

The invention discloses a metal product defect detection method and system based on image recognition. The method comprises the following steps: firstly, executing reflection disturbance digestion processing on a surface image of a to-be-detected metal product to obtain a reflection digestion image; obtaining surface reference texture features of the defect-free metal product, and generating a reference texture model on the basis of a texture distribution rule, a gray average value and texture continuity; performing defect texture gradient separation on the reflection resolution image based on a reference texture model, positioning an abnormal region, and performing segmentation to obtain a suspected defect texture region; performing boundary pixel reconstruction on the suspected defect texture region to obtain a defect texture reconstruction region; obtaining a target defect texture region through local variance enhancement and neighborhood correlation analysis; and finally, the overexposure area is positioned, gray inverse stretching processing is performed, abnormal pixel group feature clustering is performed on the preprocessed defect area, and then a surface defect detection result is output, so that the precision and accuracy of metal product surface defect detection are improved, and the false detection rate is reduced.

The invention relates to the technical field of industrial internet of things operation and maintenance, and particularly provides a remote maintenance auxiliary method integrating video monitoring and three-dimensional modeling. The method comprises the following steps: acquiring engineering graphic data and point cloud scanning data of maintenance equipment, and collecting video stream data of a maintenance equipment site; the video stream data is used for describing the operation state of maintenance equipment; the video stream data comprises a plurality of video frames; matching the point cloud scanning data with the engineering graphic data, and constructing a watertight three-dimensional grid model according to a matching result; mapping texture features of the maintenance equipment in a target video frame to the surface of the watertight three-dimensional grid model to obtain a target three-dimensional model; and receiving a first maintenance instruction marked in the target three-dimensional model by a remote expert, and sending the first maintenance instruction to a video picture of a client of an on-site maintainer. According to the technical scheme provided by the invention, the time consumption for positioning the overhaul part can be reduced.

The invention belongs to the technical field of computer vision and deep learning, and particularly relates to an optical flow estimation method and system fusing Mama and visual basic model knowledge. The method comprises the following steps: performing down-sampling feature extraction on two adjacent frames of input images by using a convolutional neural network to obtain local texture features; performing down-sampling on the first frame image to obtain context features; meanwhile, extracting global semantic features of two adjacent frames of images by using a pre-trained visual model, and performing adaptive fusion enhancement through an adaptive semantic texture feature fusion module to obtain an image coding feature pair after semantic enhancement; constructing a related volume through pixel-by-pixel dot product operation; and finally, based on the obtained related volume and context features, iteratively optimizing the output optical flow through a loop iteration updating module. The method solves the problems that in a low-texture, repeated-texture or sheltered area, feature expression is unstable, self-adaptive modeling capacity is lacked, different scenes are difficult to generalize, and model performance and efficiency cannot be balanced.

The invention provides a textile cloth defect real-time detection method and system based on multi-modal feature fusion, and the method comprises the steps: collecting visual image data, infrared thermal imaging data and ultrasonic acoustic data of textile cloth through a multi-sensor array, and forming multi-modal input; performing time sequence alignment and noise filtering preprocessing on the multi-modal data to eliminate motion artifacts and environmental interference; a parallel feature extraction module is used for extracting texture features from the visual data, extracting temperature distribution features from the thermal imaging data and extracting acoustic impedance features from the acoustic data. By deeply fusing complementary information of three modes of vision, thermal imaging and ultrasonic wave, the detection capability is improved, the vision mode captures surface texture details, the thermal imaging mode reveals thermodynamic anomalies related to friction and materials, the ultrasonic wave mode perceives subcutaneous structure defects, hidden flaws which cannot be recognized by a single mode can be found, and the detection efficiency is improved. Therefore, the omission ratio is greatly reduced, and flaw types are distinguished more accurately.

The invention relates to the technical field of industrial vision and intelligent monitoring, and discloses a loom control system operation state monitoring and fault diagnosis method based on machine vision, which comprises the following steps: acquiring a video stream in a loom shed area and constructing a two-dimensional space-time slice tensor; performing global motion compensation processing on the space-time slice tensor by using a homography transformation matrix, mapping a compensated dynamic texture feature sequence to a three-dimensional phase space by using a time delay embedding algorithm, and reconstructing a closed phase space trajectory representing periodic operation logic of the loom; the discrete Frechet distance between the phase space trajectory of the current operation cycle and the preset reference trajectory is calculated, and a control instruction is generated. The health degree of the sequential logic of the system is directly quantified on the premise that specific components are not recognized by using the invariant characteristic of the phase space manifold topology; the technical problems that small phase lag is difficult to perceive and nonlinear faults cannot be early warned in a strong noise environment are solved.

The invention relates to a computer vision processing method and system for industrial defect real-time detection. The method comprises the following steps: extracting geometric features and textural features of predefined defect types, and generating a structured descriptor set; generating a synthetic defect image set based on the defect-free image set and the structured descriptor set; inputting the synthesized defect image set into a double-flow feature extraction network to obtain a fusion feature vector; generating a defect category threshold set based on the vector and the structured descriptor set; and inputting the to-be-detected image and the corresponding defect-free reference image into the double-flow feature extraction network, calculating defect probability distribution in combination with the structured descriptor set and the dynamic classifier, and outputting a defect category decision result based on the defect category threshold set. According to the method, the precision, robustness and adaptability of defect detection are improved by means of fusing the global features of the defect-free reference image and the local features of the defect image and expanding training samples by using the synthetic defect image set.

The invention provides an infrared and visible light image fusion method based on text semantic consistency guidance, which relates to the technical field of multi-modal image fusion, and comprises the following steps: respectively carrying out fine-grained text semantic generation on infrared and visible light images and mapping the infrared and visible light images to a unified embedding space; bidirectional compensation and enhancement are carried out on text semantics through a cross-modal attention mechanism, and unified text semantic priori is constructed; a structure-intensity decoupling double-branch encoder is adopted, and structure texture features of visible light and intensity significant features of infrared light are extracted respectively; under the prior guidance of text semantics, the bimodal visual features are aligned in a shared semantic space through explicit semantic consistency constraint and implicit semantic distribution consistency constraint; and finally, taking the text semantic priori as a global modulation signal, and carrying out adaptive weighted fusion and decoding on the aligned features to generate a fused image. The problems that an existing method is insufficient in semantic modeling and poor in fusion result consistency are effectively solved.

The invention discloses an automatic cable surface defect detection method and system based on a convolutional neural network, and belongs to the technical field of computer vision and deep learning, and the method comprises the following steps: collecting an initial qualified segment image during the production of a new batch of cables, and extracting a library texture feature vector through a pre-trained texture feature coding network, unsupervised clustering is carried out, and cluster centers left after the abnormal clusters are removed and statistics of the cluster centers are stored in a normal texture self-adaptive reference library of the batch; collecting a cable surface expansion image on line, dividing the image into image blocks, inputting the image blocks into the texture feature coding network to obtain a detection texture feature vector, obtaining a texture deviation degree according to the detection texture feature vector and each cluster center in the normal texture adaptive reference library, and judging whether the texture deviation degree is a texture abnormal point or not; and carrying out connected domain analysis on texture abnormal points of the same-frame expanded image to obtain candidate abnormal regions. The problem that real defects and instantaneous noise are difficult to distinguish in the prior art is solved.

The invention relates to the field of computer vision, and discloses a defect detection method, system, medium and equipment based on adaptive dual-domain filtering and Gaussian mixture prior constraint, and the method comprises the steps: carrying out the multi-scale feature extraction of an ultrasonic C-scan image through a Vision Transform network after the ultrasonic C-scan image is preprocessed; respectively inputting the shallow fusion features and the deep fusion features into a frequency-space double-domain adaptive feature filtering module for filtering, inputting the filtered features into a Gaussian mixture modeling module Ada-GMM, and modeling normal feature distribution; carrying out Ada-GMM-Guided decoding, and carrying out interactive fusion on the corresponding deep semantic features and shallow texture features by adopting a deep and shallow multi-scale feature interaction mechanism; performing optimization by adopting cosine reconstruction loss, filtering consistency, entropy regularization loss and distribution alignment loss, and adaptively learning normal distribution characteristics according to an optimization process to obtain a model weight; and reasoning the input ultrasonic C-scan image by using the trained network weight to realize anomaly detection and positioning, and outputting an interpretable anomaly thermodynamic diagram.

The invention discloses an artificial intelligence-based equipment security and protection monitoring method, equipment and a medium, and relates to the technical field of equipment security and protection monitoring, and the method comprises the steps: constructing a deep learning optical flow estimation model based on image frame sequences, carrying out the optical flow motion vector calculation of adjacent image frames through employing the model, and extracting the motion characteristics of smoke diffusion; textural features are extracted from the image frames, normalization and time alignment are carried out on the textural features and smoke diffusion characterization features, and a space-time joint feature vector sequence is constructed; performing smoke event classification prediction on the sequence through a time sequence classification model containing an attention mechanism to obtain an event type and confidence; and triggering a security response according to the smoke event type and the confidence coefficient. According to the invention, the dynamic characteristics of smoke diffusion are effectively extracted, and the detection precision is improved; the capturing capability of time sequence continuity and space consistency in the smoke diffusion process is enhanced through spatio-temporal joint modeling, and missing report and false report are reduced.

The invention discloses a high-speed data cable product defect detection method and system based on machine vision. The method comprises the following steps: acquiring multispectral image data; denoising, enhancing and registering the multispectral image data to obtain a standard multispectral fusion image; extracting a region of interest containing potential defects from the standard multispectral fusion image based on an improved Vibe algorithm in combination with texture features of a cable, and removing a defect-free background region to obtain a region of interest image; constructing a multi-dimensional defect feature vector; and inputting the multi-dimensional defect feature vectors into the improved network model, outputting defect positions, types and confidence coefficients, automatically marking and grading cable products with defects according to an identification result, and generating a detection report. Various defects such as fine scratches, hidden oxidation and slight overheating are effectively recognized, and the recognition accuracy is improved.

The invention discloses a hydropower station unit state monitoring system based on deep learning, and relates to the technical field of hydropower station unit state monitoring and intelligent diagnosis. Comprising a multi-modal acquisition module, an electromagnetic common-mode texture anchor point generation module, an event transient skeleton generation module, a sparse anchor point index retrieval module, a continuous time joint inversion module and an analysis domain prior correction module. Generating an event stream from the mechanical side signal, and outputting an aligned candidate set to define a time mapping feasible region and a candidate time window; solving a unified time axis continuous potential signal under the constraint of missing mask information, and outputting an alignment uncertainty field; generating an alarm permission result based on the precedence relation and the delay window; and updating index parameters, threshold value strategies, priori condition parameters and iteration schedule parameters according to permission results to realize long-term stable monitoring and reliable alarm.

The invention provides a highway subgrade damage type AI recognition and classification method, system and equipment, which are applied to the technical field of artificial intelligence, and can automatically find unknown damage by obtaining a pavement image, extracting micro texture features, recognizing abnormal regions, performing unsupervised clustering and evaluating and confirming a new damage type, thereby improving the recognition efficiency. The method has the capability of automatically identifying unknown damage types, can timely discover specific damage of new material road sections, and avoids maintenance delay and cost increase.

The invention relates to the technical field of glass bottle detection, and discloses a glass bottle bottom defect detection method which comprises the following steps: building a detection platform, and adopting a multi-view imaging module of an annular LED and oblique light supplement combined light source, a vertical camera and 3-4 oblique cameras; after the to-be-detected glass bottle is positioned, synchronously collecting a bottle bottom full-view image set; graying, adaptive median filtering, CLAHE histogram equalization and Otsu binarization preprocessing are carried out on the image; extracting geometric and textural features of the image, inputting the geometric and textural features into an improved YOLOv5 model for identification and classification, and judging defects by combining with multi-view result fusion; and sorting the glass bottles according to a detection result and generating a traceable report. The identification rate of tiny defects is larger than or equal to 99%, the detection time of a single bottle is smaller than or equal to 0.5 second, and the method is compatible with glass bottles with the diameter of 30-100 mm, is suitable for large-scale quality control in the fields of food and beverage, medicine packaging and the like, and has extremely high practical value.

The invention discloses a textile dyeing defect intelligent detection method and system based on AI vision, and relates to the technical field of textile quality detection.The method comprises the following specific steps that multi-modal image collection is conducted, specifically, a multi-angle industrial camera array and a multispectral light source containing white light, blue light and near-infrared light are deployed at a production line detection station, dynamically matching and collecting the frame rate and the light source brightness through a synchronous control assembly, and synchronously collecting to form a multi-mode original image set; according to the invention, through the combined design of the double-feature separation network and the mutual exclusive attention mechanism, deep mining is carried out respectively for the geometric morphology features of the wrinkles and the color texture features of the dyeing defects, and the mutual exclusive attention mechanism effectively shields an interference channel between the two types of features by generating an exclusive mask weight matrix, so that feature separation is realized, and the detection accuracy is improved. The system can accurately distinguish wrinkles and dyeing defects, and the misjudgment risk is remarkably reduced.

The invention provides an AGV (Automatic Guided Vehicle) visual texture navigation path map real-time generation method, which comprises the following steps: collecting ground glossiness and a gray scale saturation region area, comparing the brightness difference between the gray scale saturation region and a normal region, and identifying the distribution position and the coverage range of a high-reflection region; according to the distribution position and the coverage range of the high-reflection area, extracting a texture missing part covered by the specular reflection patches, and determining boundary coordinates of the texture missing part; according to the optimized light incident angle distribution, the gray jump amplitude when the AGV drives into the polished tile area from the rough cement ground is collected, and the transition intensity of material change is evaluated; and filling a texture missing region in the path map by using the reconstructed complete texture features, and integrating the texture information of the path map to generate a complete AGV navigation path map.

The invention discloses a full-automatic glass cover plate detection system and method based on multi-sensor fusion, and belongs to the technical field of industrial automatic detection. The posture of a glass cover plate is obtained and calibrated, and a standardized input material is formed; obtaining a three-dimensional point cloud model through structured light projection and phase reconstruction, and extracting a thickness feature matrix; synchronously acquiring a visible light image and an infrared thermal image, and extracting texture features and a thermal anomaly image; mapping the multi-source data to a unified coordinate system, and constructing a micro-defect fusion map; extracting local feature vectors by using a sliding window method, and inputting the local feature vectors into a convolutional neural network to identify defect types, levels and space coordinates; high-density point cloud scanning and curvature analysis are performed on the defect area, and microscopic contour features are extracted and compared to confirm authenticity; if the defect is effective, converting the coordinate into a control parameter, and pre-marking on the surface of the glass cover plate; according to the method, high-precision, multi-dimensional and traceable defect detection is realized, and the method is suitable for an online full-automatic quality inspection scene of the intelligent terminal glass cover plate.

The invention relates to the field of computer vision and image processing, and discloses an image target recognition system based on a convolutional neural network and a feature fusion technology. Comprising a quality evaluation and alignment unit, a reversible decoupling and gating recharge unit, a multi-branch feature extraction unit, an evidence fusion unit, a marginal contribution gating unit, a topology consistency and boundary refinement unit, a detection and positioning unit and a linkage control unit. Reversible decoupling of contents and degradation components is realized in a feature domain, and bitwise gating recharge is implemented in a candidate region according to a quality map, so that small target and weak texture features are enhanced. The system fuses multi-branch output based on an evidence theory, determines weight distribution by combining marginal contribution calculation, triggers gating enhancement and local refinement when a conflict or deviation exceeds a threshold value, and realizes cooperative control of feature suppression and structure correction. According to the method, the recognition stability can be kept in complex scenes such as weak light, blurring and shielding, and the target recognition precision and the system interpretability are improved.

The invention relates to the technical field of 3D scanning, and discloses a real-time alignment highlight curved surface metallographic 3D scanning method and system, and the method comprises the steps: extracting normal vector features from a multi-view point cloud, calculating an approximate geodesic distance, and constructing a normal vector geodesic distance correlation graph; constructing a constraint propagation intensity matrix through a geodesic distance inverse ratio and a normal vector included angle exponential attenuation function, and enabling the constraint propagation intensity matrix to act on a multi-view point cloud registration residual error to generate a closed-loop error energy field; constructing a graph cut network node at the gradient extreme value of the energy field, and executing a minimum cut algorithm under geodesic constraint by taking the integral value of the energy field as the side capacity to complete global point cloud fine registration; according to the method, the local geometry and the global topology are combined through the normal vector geodesic association diagram, the problem that the registration accumulative error breaks out at the closed loop position due to the fact that the highlight curved surface metallographic surface lacks texture features is solved, and real-time accurate alignment of the highlight curved surface metallographic workpiece multi-view point cloud on an automatic detection production line is achieved.

The invention relates to an efficient concrete crack detection method, device and system. The method comprises the following steps: acquiring an image of a to-be-detected concrete beam; generating a hierarchical grid backbone network of redundant features based on linear transformation, and performing feature extraction processing on the to-be-detected concrete beam image to obtain multi-scale feature information; performing feature fusion processing on the multi-scale feature information based on a geometric perception dynamic feature pyramid network to obtain fused feature information; based on task alignment of the dynamic detection head and classification and positioning processing of the fusion feature information, the target crack information corresponding to the to-be-detected concrete beam image is obtained, the concrete crack detection accuracy is improved, the weak texture feature extraction and retention capability is significantly enhanced, the leak detection risk of fine cracks is effectively reduced, and the detection efficiency is improved. The anti-interference capability under a complex background is enhanced, the false detection rate is reduced, mismatching of classification and positioning tasks is avoided, and the crack positioning precision is improved.

The invention provides an insulator defect identification method based on cross-modal data fusion. The insulator defect identification method comprises the following steps: acquiring and preprocessing an insulator visible light image, a thermal infrared image and point cloud data which are synchronously acquired; projecting all points in the point cloud to obtain a projection image and a projection mapping matrix; registering the visible light image and the thermal infrared image with the projection image to obtain a corresponding affine transformation matrix, and constructing a shielding matrix for shielding the background according to the projection image; performing feature extraction on the pre-processed visible light image, thermal infrared image and point cloud data to respectively obtain a texture feature map, a temperature feature map and a geometric feature map; fusing the texture feature map, the temperature feature map and the geometric feature map according to the shielding matrix and the affine transformation matrix to obtain cross-modal fusion features; and the cross-modal fusion features are used to discriminate and position insulator defects. According to the method, the problem of leak detection of insulator defects in different scenes by a traditional single-mode method can be solved.

The invention provides a road three-dimensional lane line detection method and system based on binocular vision, and relates to the technical field of automatic driving, and the method comprises the steps: carrying out the online calibration of internal and external parameters of a camera and the time sequence alignment of the binocular image and IMU attitude data through collecting the forward binocular image and IMU attitude data of a vehicle; processing the binocular image after time sequence alignment, and determining a dense disparity map; generating a road point cloud based on the dense disparity map and camera parameters, and constructing an adaptive terrain model; constructing a depth enhanced BEV feature map according to the depth feature of the road point cloud and the texture feature of the binocular image, predicting candidate parameters of a three-dimensional lane line, and screening through various constraints; and then executing extended Kalman filtering and trajectory optimization to obtain three-dimensional lane line parameters. According to the invention, rapid and accurate detection of the three-dimensional lane line can be realized, the accuracy of three-dimensional lane line detection under a complex terrain is improved, and the deployment cost is reduced.

The invention provides a cattle and sheep feed trough residual feed monitoring method based on vision, and relates to the technical field of intelligent livestock breeding, and the method comprises the following six steps: intelligent triggering and multi-modal image acquisition, image preprocessing and fusion, time sequence image segmentation and feature extraction, density adaptive volume calculation, online learning and residual feed estimation, and decision analysis and early warning. According to the method, RGB and near-infrared images are synchronously collected through infrared triggering, after perspective correction and illumination adaptive fusion, an LSTM-U-Net time sequence segmentation model is adopted to solve the dynamic shielding problem, a residual feed area is accurately extracted, the feed type is recognized, density adaptive volume measurement is achieved in combination with monocular depth estimation and texture feature analysis, and the method is suitable for large-scale industrial production. Weight estimation is carried out through a support vector regression model, model parameters are optimized on line based on manual correction data, intelligent early warning of the residual material amount is finally achieved through a cloud platform, and the limitation of a traditional method in the aspects of shielding processing, density adaptation and environment anti-interference is effectively overcome.

The invention relates to the technical field of image processing, and discloses a printing defect detection method based on image processing, and the method comprises the steps: generating an illumination distribution diagram according to the regional illumination distribution characteristics of a printed matter image, and carrying out the self-adaptive illumination correction of the printed matter image, and obtaining a corrected image; performing multi-scale space decomposition on the corrected image to obtain a first scale texture feature and a second scale texture feature; based on a preset space reference, performing granularity alignment on the first scale texture feature and the second scale texture feature to obtain a fused texture feature; performing multi-modal feature coupling on the contour feature of the corrected image and the fused texture feature to obtain a comprehensive feature, and identifying a potential defect region according to the difference between the comprehensive feature and a reference template; based on the reference template, performing multi-dimensional feature recognition on the potential defect area to obtain a defect area; according to the invention, the printing defect detection efficiency of image processing can be improved.

The invention relates to the technical field of computer vision, in particular to a fabric color fastness analysis method and system based on image processing. Comprising the following steps: acquiring a multispectral image of a to-be-detected fabric, and synchronously acquiring multi-factor data; processing the multispectral image to generate an enhanced image; performing image recognition on the gray sample card in the enhanced image to generate a calibration image; performing spectral data projection on the calibration image through a dimension reduction algorithm, and performing color analysis and quantification on color change to generate color difference features; extracting texture features by using a conditional generative adversarial network, carrying out image recognition and calculation on the calibration image through a decoupling algorithm, and generating a region credibility graph; and inputting the chromatic aberration features, the texture features, the multi-factor data and the regional credibility map into an image feature fusion model for mapping, performing analysis through gradient visualization, and outputting an abnormal feature saliency map. According to the method, a color fastness objective analysis closed loop is created, so that the accuracy and the universality of an analysis result are improved.

The invention relates to the technical field of image processing, and discloses a tea maturity detection method and system, and the method comprises the steps: obtaining an original image of tea in a natural illumination imaging environment; generating a global illumination difference chart representing an ambient illumination state, and performing illumination compensation on the to-be-detected area of the tea in the original image to obtain a compensated image of the tea; performing gray scale range adjustment on the compensated image of the tea leaves to obtain an enhanced spectral image of the tea leaves; integrating the color and spectral characteristics of the specific spectral reflection characteristics of the tea leaves with different maturity degrees in the enhanced spectral image and the three-dimensional morphological characteristics reflecting the spreading degree and thickness of the tea leaves into the fusion image characteristics of the tea leaves; mapping the color spectrum emission characteristics and texture characteristics of the tea leaves in the enhanced spectrum image to a maturity discrimination knowledge base of the tea leaves to obtain a preliminary maturity result; performing confidence coefficient weighting on the preliminary maturity result to obtain a target maturity; according to the invention, the tea maturity detection efficiency can be improved.