486 results about "Color recognition" patented technology

The invention belongs to the field of environment monitoring and computer vision, and particularly relates to a water body color recognition regression method and system based on space-time causality and manifold learning, and the method mainly comprises the steps: carrying out the detection of a current target water body video sequence, extracting a water body region, carrying out the high-dimensional feature dimension reduction of the water body region, and obtaining a water body color recognition result; and performing feature extraction on the water body region through a space-time causal feature learning model, fusing the flow shape learning features and the space-time causal features to obtain fused features, and outputting a finally predicted water body color value. According to the method, end-to-end assembly line design of preprocessing-segmentation-feature modeling-regression is adopted, manual intervention is not needed from video input to color prediction, and through cascade cooperation of five core modules (video preprocessing, water body segmentation, manifold learning, time sequence causal modeling and color recognition), the real-time performance of the system is improved. Full-link automation from environmental interference suppression, feature extraction to result output is realized, information loss of intermediate links is avoided, and recognition efficiency and robustness are improved.

The invention relates to the technical field of textile detection, and provides a fabric sticky hair identification method and identification system based on fluorescence enhancement and machine vision, and the fabric sticky hair identification method comprises the following steps: carrying out fluorescent dyeing on a fleece when the fleece is dyed; rubbing the fuzzing cloth with a to-be-detected fabric so as to enable the to-be-detected fabric to be fuzzed; placing the to-be-detected fabric in a preset environment so as to enable the sticky hair on the to-be-detected fabric to emit fluorescence; acquiring a surface image of the to-be-detected fabric after hair sticking and fluorescent color development; analyzing the area proportion of a fluorescent region in the surface image based on image color identification; and according to the area proportion of the fluorescent region, determining the hair sticking grade of the fabric to be detected. According to the invention, the problem that the precision and reliability of sticky hair detection are seriously influenced by material and color differences of a machine vision detection system in the prior art can be solved.

The invention belongs to the technical field of optical lenses. The invention relates to a butterfly pupil escape compound eye bionic high-color-gamut nylon sunglass lens which comprises a lens body and a coating. Wherein the lenses are made of nylon resin; the coating is prepared from the following raw materials in parts by weight: 25 to 40 parts of tetraethoxysilane; 10 to 20 parts of phenyl trimethoxy silane; 4.0 to 8.0 parts of dimethyl dimethoxy silane; 2.0 to 4.0 parts of an MOF color enhancing agent; 20 to 30 parts of isopropanol; 11 to 22 parts of deionized water; and 1.0-3.0 parts of glacial acetic acid. A coating is prepared from a self-made MOF color enhancing agent through molecular design through formula optimization design, and the nylon resin lens is coated with the coating, so that the butterfly pupil escape compound eye bionic high-color-gamut nylon sunglasses lens is obtained. The butterfly pupil escape compound eye bionic high-color-gamut nylon sunglass lens effectively solves the problem that the color identification degree is low after the butterfly pupil escape compound eye bionic high-color-gamut nylon sunglass lens is blocked by 45-90% of light rays at present.

The invention relates to an intelligent environment color recognition method, which realizes accurate control of a color recognition technology under the working condition of a sea-land dual-purpose sound and temperature hidden transfer device, and comprises the following steps that: an image acquisition module acquires various kinds of environment image data; the preprocessing module optimizes the image quality and the data format; the color feature extraction module extracts multi-dimensional features by using a deep learning algorithm; the deep learning model module learns a mapping relation through mass data training; the color recognition module feeds back and accurately recognizes colors based on the training model and the confidence coefficient; and the result output module displays the result in a plurality of visual modes. The environment color recognition method improves the color recognition accuracy and adapts to complex environment illumination and color combination; the method has good adaptability and is suitable for various scenes; reliable data processing is guaranteed, real-time performance, stability and personalized control are achieved, and user experience and working efficiency are improved.

The invention discloses an indicator light multi-state cascade identification method based on a deep learning shared network, and belongs to the field of intelligent inspection of power equipment. According to the method, low-order textures and spatial features of an image are extracted through a unified convolutional neural network, and a shared feature map is formed; a channel separation mechanism is adopted, the shared feature map is distributed to color recognition and state detection parallel branches according to dimension weights, the form discrimination branch on the uppermost layer analyzes geometric contour features through a full-connection layer, and circular / strip-shaped classification is achieved; the color identification branch adopts a dual-channel convolution kernel to respectively strengthen red / green spectral response, and color judgment is completed through feature map comparison; state detection branches fuse space pooling and brightness statistical analysis, and a decision boundary of an on-off state is established. According to the method, parameter redundancy can be reduced, the identification precision of each sub-task is improved, and the requirement of the inspection equipment for real-time judgment of a multi-dimensional state is met.

The invention relates to a computer vision technology, and discloses a laying hen health condition monitoring method and system based on a cockscomb form, and the method comprises the steps: collecting laying hen cockscomb images according to a preset time interval, and forming an image sequence; and carrying out gravity center identification on each image in the sequence, obtaining gravity center sequence data and analyzing gravity center offset. If the center-of-gravity offset exceeds a preset threshold value, color identification is directly carried out on the current image to obtain color data, and if the center-of-gravity offset does not exceed the preset threshold value, the form variation of the cockscomb edge area in the image sequence is analyzed. When the form variation is greater than a preset threshold value, judging that the laying hen is healthy; and if not, returning to execute the color identification step. And finally, performing disease judgment according to the color data to obtain a judgment result. The laying hen health condition monitoring efficiency can be improved.

The invention discloses a multi-channel integrated marketing contact content management system and method, and relates to the technical field of marketing contact content auditing, and the method comprises the steps: creating a plurality of marketing contacts in a unified management platform, and configuring a delivery channel; establishing a design specification rule base; receiving a marketing content picture uploaded by the user and associating the target channel with the contact; calling a rule base on the basis of a contact type, starting an AI auditing engine to carry out automatic compliance inspection on the picture, including preprocessing, layout identification, character extraction and character number statistics, font and character size identification, color identification and functional element detection, and comparing an identification result with a rule to generate an auditing result; and approving the delivery of compliant content and tracking the effect, and optimizing an AI engine and a rule base based on data. According to the method, AI intelligent auditing is embedded into the marketing content publishing process, multi-channel integrated management and intelligent auditing of the marketing content are achieved, the auditing efficiency and accuracy are remarkably improved, it is guaranteed that the content is standard and unified, and the labor cost is reduced.

The invention discloses an electric vehicle rear-end collision accident prediction method and system based on large model knowledge and trajectory data. According to the method, two vehicle types of an electric vehicle and a fuel vehicle are identified based on license plate colors, the vehicle types and vehicle tracks are bound, and the following process is divided into three states according to the combination of the front and rear vehicle types. On the basis, multi-dimensional features extracted from historical tracks are input into a large language model, expert-level rule knowledge used for distinguishing accidents and non-accidents is extracted through structured cues, and a final feature matrix is constructed according to the knowledge. And finally, based on the final feature matrix, performing differentiation processing on the three car-following states, training and calibrating a dichotomy model, and realizing real-time estimation of the rear-end collision probability at a specific moment in the future. According to the invention, by fusing the reasoning ability of the large model and the real trajectory data, the accuracy and interpretability of rear-end collision risk identification under the electric vehicle participation scene can be significantly improved, and the method has engineering deployment value.

The invention discloses a soil color exploration and identification system and method, belongs to the technical field of soil color identification, can effectively reduce the influence of ambient light on soil color discrimination through a standardized color correction board and software image processing, and performs hierarchical division on a soil color identification image along a vertical plane, thereby improving the accuracy of soil color identification. Pixel point color information of a plurality of independent soil layers is obtained, so that the color distribution uniformity of a single layer is judged, then the color distribution condition of the whole image is comprehensively analyzed to divide soil types, and different color recognition modes are selected according to the judged soil types to be compared with a colorimetric card digital image. Compared with a mode of directly extracting color information of an overall image and comparing a colorimetric card, hierarchical division is adopted, single-to-overall soil type judgment is carried out, and a proper recognition mode is selected according to a judgment result, so that the color recognition accuracy is effectively improved.

The invention provides a vision-based paper anomaly detection method and device, and the method comprises the steps: configuring a template drawing for a detection region, determining a detected effective region mask according to the template drawing, and setting a counting standard-reaching parameter; collecting a preset number of initial environment images in the detection area for initial judgment when detection is started; after the initial judgment is completed, collecting a real-time environment image according to a preset time interval, determining an effective area and an invalid area in the real-time environment image according to the effective area mask, performing color recognition on the effective area in the real-time environment image, and performing frequency domain calculation on the invalid area; according to an effective area color identification result and an invalid area frequency domain calculation result of the real-time environment image and the counting up-to-standard parameter, whether paper breaking and shielding occur currently is judged; the method can improve the reliability of broken paper detection.

The utility model relates to the technical field of toys, in particular to an interactive educational toy for children, which comprises a support and a plurality of pressure sensors fixedly connected to the top of the support at equal intervals. And the number of the storage assemblies is multiple, the number of the storage assemblies is equal to that of the pressure sensors, each storage assembly comprises multiple fixing rods and multiple snowflake building blocks, and baffle rings are fixedly connected between the upper end faces of the multiple fixing rods. According to the snowflake building block toy, after a child finishes an educational toy snowflake building block game, parents can guide the child to store and arrange the snowflake building blocks by themselves, the loudspeaker can send out audio frequency to guide and encourage the child to store the snowflake building blocks, and the child can improve color recognition and manual operation ability through interactive storage. And a good habit of self-storage of personal articles can be developed, and a good interactive education effect is achieved.

The invention discloses a high-precision color identification method for a complex light source environment, and relates to the technical field of color identification. The method specifically comprises the following steps: 1) constructing a hardware system; 2) carrying out image preprocessing; 3) extracting image features by using an improved YOLO algorithm, performing multi-scale feature fusion by using a feature pyramid network, and correcting color distortion caused by a light source, equipment or a material in combination with a color restoration algorithm to ensure real restoration of the color; 4) designing a color evaluation algorithm, outputting color parameters such as a Lab value and a delta E value, accurately evaluating the color, and performing color difference comparison; 5) establishing a standard database, supporting the learning and optimization of historical data, and improving the identification accuracy of a new sample; 6) an automatic operation process; according to the method, advanced hardware configuration, deep learning, color restoration and automatic control technologies are combined, high-precision color identification in a complex light source environment is facilitated, and the method has a wide application prospect.

The utility model relates to the technical field of color sorters, and discloses a color sorter which comprises an inclined plate, a hopper is fixedly connected to one side of the upper end of the inclined plate, a discharging roller is rotatably connected to the bottom end of the interior of the hopper, a plurality of material distributing grooves are formed in the outer wall of the discharging roller in an annular array mode, and a discharging opening is formed in the bottom end of the hopper. And a shading plate is fixedly connected to the upper end of the inclined plate, a plurality of visual cameras arranged in a linear array are arranged at the upper end of the shading plate in a penetrating mode, and light supplementing lamps are fixedly connected to the two sides of the interior of the shading plate. According to the color sorter, through the design of the discharging roller and the material distributing groove in the hopper, materials can be evenly and continuously separated and conveyed to the inclined plate, high-speed and accurate color recognition is conducted in cooperation with the visual camera, the sorting efficiency is improved, meanwhile, the light supplementing lamp provides even and sufficient light, it is ensured that the visual camera can capture clear material images, and the color sorting efficiency is improved. Therefore, the screening precision is improved.

Battery life cycle management is facilited with distinct visual cues of colors and color-coded combinations. The color-codes enable battery types and characteristics over the course of the life of the battery to be determined. The color-codes are based on a color system that indicates a performance rating of the batteries. A system for managing the color-coded batteries selects color-coded batteries for use based upon color, maintaining color based functionality, and color-coded battery indication for destruction of the battery at the end of its life cycle.

In order to solve the problem of intelligent screening of colors of waste glass, the invention designs an intelligent screening device for waste glass, and the device is sequentially provided with an electromagnetic vibration feeder, a chute, a light source, an image acquisition and recognition device, a pneumatic injection device and a glass classification storage device from an input end to an output end. According to the method, the colors of the waste glass particles are collected, the colors of the waste glass particles are recognized through an image recognition and processing algorithm, and then the waste glass is classified and screened by controlling a pneumatic injection device according to the recognition result. The intelligent waste glass color screening system which is convenient, rapid and high in screening precision is provided for waste glass color screening.

The invention discloses a game anti-plug-in Ai algorithm ESP protection mechanism, which is used for protecting game speed, blood volume encryption, equipment encryption, simulated mouse encryption, mouse track analysis, game character blood strip encryption and infinite blood strip technology, and enabling a client to support speed control, vulnerability repair, infinite blood encryption and infinite equipment encryption expansion. According to the method, the game client can be effectively prevented from being modified and invaded by the plug-in, and the security and integrity of the game data are protected. The AI behavior detection technology can recognize abnormal behaviors in real time, the attack difficulty of the plug-in is further improved through the functions of a speed sealing system, an anti-simulation keyboard and mouse and the like, the use scenes of the plug-in are reduced through the measures of multi-open control, virtual machine detection and the like, and the graph color type on-hook behaviors are effectively attacked through the graph color protection function. The whole protection mechanism combines data encryption, an AI algorithm and multi-dimensional protection control, a set of complete game anti-plug-in solution is formed, and powerful guarantee is provided for fairness and stability of games.

The invention discloses an eye disease analysis method based on an eye image, and the method comprises the steps: carrying out the image segmentation of a sclera region through employing a convolutional neural network architecture u-net network model of image segmentation, carrying out the recognition of a hyperemia part of the sclera region, and calculating the proportion of the hyperemia part through employing FrangiFilter blood vessel recognition in combination with the color recognition of an LAB color space; by combining the image algorithm with a traditional image processing and deep learning model, the segmentation precision of the sclera region, the identification precision of sclera congestion, the robustness, the high accuracy and the rating accuracy are improved under the condition that the operation speed is not reduced.

The invention relates to the technical field of wire core wires, and discloses a wire core wire color identification and tiling method which comprises the following steps: 1) identifying an initial position of a set core wire through a camera, and rotating the set core wire to a set position; 2) arranging the plurality of core wires side by side in sequence; 3) pulling the plurality of core wires into a straight line shape; the initial position of a set core wire is recognized through a camera, the set core wire is rotated to the set position through rotation, core wire color recognition operation is achieved, then a plurality of core wires which are spatially staggered abut against the same plane through a flattening structure so that the core wires can be sequentially arranged side by side, and then the core wires are movably clamped through a straightening structure. And the core wires are pulled linearly relative to the plurality of core wires, so that the plurality of core wires are pulled to be linear, the plurality of core wires are tiled, the operation efficiency is high, and the cost is greatly saved.

The invention discloses an intelligent regional refrigeration adjusting system and method based on multi-mode sensing, relates to the technical field of refrigerated cabinets, and aims to solve the problem that a traditional refrigeration device only conducts passive adjustment through pressure and temperature and cannot sense material decay in the early stage. The system comprises a pressure / temperature acquisition module, a state sensing module (a color recognition unit and a smell recognition unit), a grading decision module and a cold supply adjusting module, and material states are divided into L0-L3 four levels; according to the method, multi-modal data are synchronously obtained through a depolarization method, color / smell change indexes are obtained after processing, state grades are judged, customized cold supply instructions (such as L1 cooling and wind speed increasing and L3 limit refrigeration and isolation) and early warning instructions are generated in combination with pressure and temperature, and the instructions are independent in different areas.

The invention discloses an image resolution monofilament detection system and method, and relates to the technical field of monofilament detection. According to the image resolution monofilament detection system and method, a high-resolution camera is used for collecting a marked area image caused by an identification accessory on a chemical fiber godet wheel in real time, an image processing module is used for carrying out noise removal and color space conversion on the image, and a Canny edge detection algorithm is used for extracting the edge contour of the image. And the color identification module analyzes the color change of the marked area through a contour tracking algorithm, calculates to obtain a broken wire index, and judges whether the marked area is a broken wire area according to a preset threshold value. The broken wire judgment module judges whether the wire guide wheel rotates or not according to the color recognition result so as to judge whether the wire is broken or not, and early warning information is sent out when the wire is broken. The system can accurately monitor the yarn breakage problem in the chemical fiber production process, and has efficient and real-time yarn breakage detection capability.

The invention discloses a line sequence automatic detection algorithm based on machine vision and an implementation method thereof, and belongs to the technical field of machine vision and image processing. Aiming at the problems of low efficiency and poor accuracy of traditional manual detection of a multi-core cable, the invention provides the following technical scheme: preprocessing a collected cable image, and enhancing the image by adopting adaptive histogram equalization of an LAB color space; determining a scanning band at the center of the image and converting the scanning band to an HSV color space; the comprehensive value of the HSV color gradient is calculated, the peak value is detected by adopting an adaptive threshold value, and the boundary of each wire core is accurately positioned; establishing an HSV (hue, saturation and value) feature library containing 10 standard colors, and realizing color matching through weighted distance measurement; and an automatic detection mode and a manual assistance mode are provided. According to the method, the detection accuracy reaches 95% or above, the processing time of a single image is shorter than 1 second, the technical problems of automatic color recognition and sorting detection of the multi-core cable are effectively solved, and the method can be widely applied to cable production quality control.

The invention provides an image color recognition method, computer equipment and a storage medium, and the method comprises the steps: obtaining a to-be-recognized image, and extracting a sky region in the to-be-recognized image; a target color area is determined in the sky area, image features of the to-be-recognized image are determined based on the target color area and the sky area, and the color of the target color area is in a preset color interval; and when it is determined that the to-be-identified image satisfies a toning condition based on the image features, toning the sky area. In the embodiment of the invention, the influence of other area pixels, such as ground area pixels, in the to-be-identified image on image feature extraction is eliminated, so that the reliability of determining whether the to-be-identified image meets the toning condition or not based on the image features is improved, and when it is determined that the to-be-identified image meets the toning condition, the toning accuracy of the to-be-identified image is improved. And toning the sky area in the to-be-identified image, or not executing toning operation when the to-be-identified image does not meet the toning condition.

The invention provides a metal product surface paint spraying color quality inspection method based on deep learning, and the method comprises the following steps: 1, determining a to-be-detected metal workpiece paint spraying color and a standard color card color, collecting a data set, carrying out the processing and data labeling of the data set, and carrying out the format conversion of a generated labeling file through a script; 2, a color recognition model is constructed, namely, YOLOv5 and Resnet-18 are used for color recognition, and color type recognition of the metal workpiece after image input is achieved; step 3, constructing a color consistency evaluation model, that is, performing target segmentation by using a U-net network, and then performing twin neural network training to perform qualification inspection; 4, adjusting the parameters of the improved model to obtain a network model, and comparing the performances of the two models; and finally, shooting a workpiece picture under an actual condition to verify the applicability of the model, and analyzing the advantages of the model in different scenes. By applying the technical scheme, the pixel-level segmentation prediction of the workpiece can be realized.

Apparatus and associated methods relate to image generation systems. In an illustrative example, an image capturing module (ICM) may be configured to capture a real-time image of a target object (e.g., a car). The ICM, for example, may include a gyro-sensor and a user interface. For example, in a studio mode, the ICM may automatically generate a 360° image of the target object as a function of the gyro-sensor measurement. In a guided capture mode, for example, the ICM may generate guidance indicia at the user interface to assist in capturing components of the target object. In some implementations, the ICM may automatically apply image adjustments based on pixel segmentation and color identification such that the images captured are according to a predetermined image standard profile. Various embodiments may advantageously automatically be captured and standardized images of a target object based on the real-time image.

The anti-counterfeit detector allows to recognize authentic products (handbags) and to distinguish them from counterfeits. It is composed of 3 element chips allowing the recognition of branded products. - An electronic chip dedicated to the gates called Smart Electronic Chips for Gate (3) [Fig1], (1) [Fig2] - An electronic chip dedicated to the gates called Smart Electronic Chips for reader by waves for barcode scanner (2), (3) [Fig1], (2), (3) [Fig3] - An electronic chip dedicated to the gates called Smart Electronic Chips for barcode reader for barcode scanner (2) [Fig1] [Fig3] [Fig4] The control of counterfeiting is carried out thanks to the recognition / authentication of at least three of the criteria below: a. Recognition of an identical shape b. Recognition of an identical color c. Recognition of a specific sign

The invention provides an automobile coating query method, which comprises the following steps: acquiring a full-surface image of a to-be-queried automobile, and determining the color complexity of the to-be-queried automobile based on the full-surface image; based on the color complexity of the to-be-queried automobile, matching a target feature extraction model corresponding to the color complexity in a model library; performing feature extraction on the full-surface image through a target feature extraction model to obtain appearance color features of the to-be-queried automobile; and querying paint data of the to-be-queried automobile in a paint database according to the appearance color characteristics. By introducing a color complexity evaluation mechanism, intelligent analysis of the full-surface image of the automobile is realized, and the most suitable feature extraction model is dynamically selected according to the analysis result, so that the color recognition accuracy is improved, the waste of computing resources is effectively reduced, the paint query time is greatly shortened, and the paint query efficiency is improved. And the overall automobile paint query efficiency is improved.

The invention provides a color identification method and system for allium chinensis and sorting equipment. The method comprises the following steps: acquiring an allium chinensis image; performing background segmentation on the allium chinensis in the allium chinensis image to obtain an allium chinensis segmented image; identifying a white area in the segmented allium chinensis image; and according to the allium chinensis segmented image and the white area, reversely deducing a defect area to obtain a mixed-color area. The method can effectively identify the mixed color area of the allium chinensis, and improves the accuracy of allium chinensis color identification.

The invention discloses a pavement natural color seeing detection method based on deep learning, and relates to the technical field of deep learning, and the monitoring method comprises the following steps: collecting the pavement material of each road, dividing each road according to the pavement material, and obtaining a plurality of types of road sets; a stored road surface shot picture is obtained, a color recognition model is constructed to recognize the road surface shot picture, and a color value is obtained; randomly selecting two detection records from the detection log, and analyzing the difference of the two detection records to obtain an influence coefficient of each influence factor on a color value; the influence coefficient of each influence factor on the color value is analyzed, the color value is calibrated, and the error range of the same kind of road set is obtained; when a road is detected, generating a detection record of the road; and comparing the actual characteristic value and the theoretical characteristic value of the detection record, and judging whether abnormity reminding is carried out on the road or not.

The utility model discloses a storage box for storing pens or refills, and relates to the field of graph drawing. The utility model solves the problems that a gel pen refill or a ball pen refill used by the existing compasses is not provided with storage and color identification, so that color identification is inconvenient for a user, and the gel pen refill or the ball pen refill is easy to lose. The utility model discloses a slot (1), upper cover (2) and bottom groove (3), the slot (1) is detachably clamped on the bottom groove (3), the upper cover (2) and the bottom groove (3) can be opened and buckled, and the slot (1) is used for inserting a gel pen, a ballpoint pen, a gel pen refill or a ballpoint pen refill. The slot (1) is a flat hollow rectangular slot. A plurality of jacks (4) are uniformly distributed on the slot (1), the plurality of jacks (4) are arranged in one row or two rows, and the number of the jacks (4) in each row is 8-12. The multifunctional pen container is used for containing colored gel pens or ball-point pen refills used in the graph drawing process.

The application discloses an aggregation-induced emission metal organic framework, a preparation method thereof and fluorescent / colorimetric dual-mode intelligent detection for trace HCl. 2+ The aggregation-induced emission metal organic framework is prepared by providing Zn(NO3)3.6H2O as Zn 2+ , imidazole-2-formaldehyde and an AIE molecule HDBB as organic ligands, and through a solvothermal method to obtain ZIF-90-HDBB, which can change the aggregation state fluorescence from yellow green to orange red after being contacted with HCl gas; the ZIF-90-HDBB is uniformly loaded on cellulose filter paper to obtain a paper-based sensor for portable detection of HCl, and through combination with a color recognition APP of a smart phone, the HCl can be rapidly and sensitively detected on site in a fluorescent / colorimetric dual mode. Since the HDBB molecule is prone to protonation under acidic conditions, the fluorescence color changes, and therefore the material can be accurately and efficiently used for detection of acidic gas, and the material can be recycled.