807 results about "Color correction" patented technology

The invention provides a remote sensing strip mine area detection method based on a double-branch structure and a feature fusion mechanism, and the method comprises the steps: obtaining multi-source high-resolution remote sensing image data of a strip mine mining area, carrying out the preprocessing of atmospheric correction, radiation calibration, color correction, image registration, cutting operation and the like, and obtaining time sequence remote sensing image data; a deep learning algorithm is adopted to construct a strip mine area detection model based on a double-branch structure and a feature fusion mechanism, training is carried out through the time sequence remote sensing image data, a remote sensing image detection model is obtained, the double-branch structure comprises a feature extraction branch and a feature generation branch, and the feature extraction branch comprises a feature extraction branch and a feature fusion branch; the feature fusion mechanism comprises a cross attention fusion module and a feature adaptive fusion module; inputting to-be-detected remote sensing image data into the remote sensing image detection model to obtain a detection result of the strip mine mining area. According to the method, the recognition precision of small target details and mining area boundaries of low-resolution images is improved, and the calculation efficiency and the detection precision are both considered.

The invention discloses a panoramic image real-time splicing algorithm and system based on multi-sensor fusion, and particularly relates to the technical field of panoramic image real-time splicing, and the algorithm comprises the following steps: constructing a structured fusion sequence based on multi-source images, postures and position information, optimizing a matching effect through high-density feature extraction and repeated texture recognition, and obtaining a multi-source image fusion sequence; a dynamic foreground and a static background are distinguished by using sparse optical flow so as to improve the visual angle estimation precision, pose fusion optimization is realized in combination with a multi-mode residual error, and the continuity and stability of a spliced image are improved through edge smoothing, brightness tuning and color correction; according to the method, the structured fusion sequence is constructed through multi-source data alignment, so that the data synchronization and splicing stability is improved; identifying repeated regions based on texture direction features, and optimizing feature matching accuracy; and through edge smoothing, brightness harmonizing and color consistency processing, the visual coherence and output quality of the panoramic image are enhanced.

The invention discloses a tunnel detection method based on a color image, and relates to the technical field of tunnel detection, and the method comprises the following steps: obtaining color image data in a tunnel, and carrying out the illumination condition calibration of the image data, so as to recognize the illumination change caused by natural light, an artificial light source and a light source fault. According to the method, through illumination calibration, adaptive color correction and dynamic contrast enhancement technologies, the problems of image color distortion and detail loss under the complex illumination condition of the tunnel are solved, accurate extraction of color features and clear presentation of details of each region are ensured, and the robustness and precision of defect detection are remarkably improved. In combination with edge detection, color feature extraction, shape analysis and a machine learning algorithm, automatic application of a multi-modal identification technology is realized, misjudgment areas are automatically filtered, a defect detection report with high confidence is generated, and the defect detection report comprises crack length, leakage area and deformation classification. Accurate and comprehensive data support is provided for tunnel safety assessment and maintenance decision.

The invention relates to an underwater image processing technology in the field of underwater autonomous perception, in particular to an underwater low-quality image enhancement method based on a Laplacian pyramid and contrast learning. Comprising the following steps: S1, image input and multi-scale decomposition: decomposing an input image into a low-frequency residual layer and a plurality of high-frequency detail layers through Laplacian pyramid decomposition; s2, inputting the low-frequency residual error layer into a global illumination and color correction sub-module to obtain an enhanced low-frequency residual error layer; s3, inputting the enhanced low-frequency residual error layer and the multi-scale high-frequency detail layer into a frequency domain enhancement feature module to obtain multi-scale enhanced high-frequency detail layer output; s4, performing progressive reconstruction on the enhanced low-frequency layer output and the high-frequency enhancement layer output of each scale according to the inverse process of the Laplacian pyramid; and S5, outputting a result and applying. The method can be used for sharpening optical image data in the operation task of the autonomous underwater vehicle, and the image quality is improved.

The invention relates to an image processing method for an underwater robot heterogeneous imager array, and the method comprises the steps: S1, collecting an original underwater image, and carrying out the brightness adjustment, and obtaining a first image with balanced brightness; s2, analyzing pixel distribution of the first image, and judging and executing de-scattering processing to obtain a second image; s3, performing multi-scale detail enhancement on the second image to obtain a third image with richer details; s4, evaluating color distribution of the third image, and performing color correction to obtain a fourth image; s5, performing adaptive denoising processing on the noise features of the fourth image to obtain a fifth image; s6, according to the contrast characteristic of the fifth image, contrast adjustment is carried out to ensure that a preset threshold value is met, and a sixth image is obtained; s7, edge enhancement processing is carried out on the sixth image, and a seventh image with the clear edge is obtained.By means of illumination compensation, scattering removal and multi-scale detail enhancement, the influence of complex optical conditions of the underwater environment on the image quality is effectively overcome.

The invention relates to the technical field of image processing, in particular to an image acquisition and analysis method and system, and provides the following scheme: obtaining a visible light and near-infrared multispectral image, generating a spectral difference image, and performing weighted fusion to obtain a first image; segmenting a target region based on the fused saliency map, and calculating a pixel reflectance ratio; solving a color mapping matrix according to the reflectance ratio, and carrying out color correction on the target region to obtain a standardized feature image; and extracting characteristic parameters such as spectrums, colors and textures, inputting the characteristic parameters to a multi-branch convolutional neural network, fusing the characteristic parameters through an attention mechanism, and outputting a state classification result and a quantitative index. The cross-spectral imaging difference can be adaptively compensated, and the fusion precision and the analysis stability are improved.

The invention relates to the technical field of optical detection, in particular to an AOI automatic optical detector calibration method and system, and the method comprises the following steps: obtaining a standard color palette RGB reflection reference, collecting a measured value, calculating an offset contrast, extracting image brightness, generating a correction ratio, converting gray level, extracting abrupt change, screening anomalies, and analyzing a brightness trend to generate a finishing record. And formulating a rule matching correction output comparison table. According to the method, the accuracy and reliability of image detection are enhanced through detailed analysis and accurate correction of color offset and brightness difference, the scheme combines real-time light source data and image information, fine changes are effectively identified, the misjudgment rate is reduced, the accuracy of gray and color correction is improved through comprehensive analysis of light intensity offset and regional brightness trend, and the accuracy of image detection is improved. The detection efficiency and quality of a high-density circuit are ensured, the detailed calibration mechanism remarkably reduces detection errors especially in a high-precision environment, and product quality control and the stability of a production process are optimized.

The invention belongs to the technical field of video processing, and particularly relates to a monitoring video enhancement method for a farm, which aims to solve the technical problem of low quality of an enhanced video in the prior art, and comprises the following steps: S1, processing each frame of image in a monitoring video sequence frame by frame; s2, distinguishing a target animal area from a background area, and identifying and generating an artifact mask; s3, aiming at the background area, carrying out key smoothing processing on the artifact position to inhibit the artifact; s4, for the image of the target animal area, performing adaptive nonlinear enhancement on the brightness component, and performing color correction on the chrominance component; s5, performing pixel-level fusion on the enhanced target animal area and the background area; and S6, spreading the information of the previous frame to the current frame by using the forward optical flow field, and carrying out weighted fusion on the information of the previous frame and the current frame. According to the method, the target bred animals, the background areas and the artifacts are accurately distinguished, so that refined and differentiated processing of pictures is realized.

The invention provides a multi-scale polarization data fusion method based on a 4K polarization imaging detector, and relates to the technical field of image processing, and the method comprises the steps: carrying out the adaptive edge detection of four original polarization images, generating a comprehensive edge image, and carrying out the definition scoring; performing color calibration on each original polarization image based on the ambient light intensity and the color temperature, and outputting a polarization image after color calibration and a color deviation score; combining definition scores, color deviation scores and global brightness information entropies of the four polarization images after color calibration to construct a multi-objective optimization function, and solving the multi-objective optimization function in a multi-exposure parameter space to obtain an optimal exposure parameter combination; applying the optimal exposure parameter combination to image capture of the next period, and calculating global quality reward values of four newly collected polarization images; when the global quality reward value meets a preset condition, wavelet decomposition and frequency division multi-scale fusion are carried out on the polarization images after color correction, a fused image is generated, and the quality of the polarization images is effectively improved.

The invention discloses a video image color correction method and system based on artificial intelligence, and relates to the technical field of color correction, and the method comprises the steps: collecting video data, carrying out the image size normalization and denoising processing, and obtaining the preprocessed video frame sequence data; and extracting reflectivity data and illumination data by using a Retinex-Net deep learning model, calculating an illumination adjustment coefficient, carrying out pixel correction, and carrying out color temperature correction on the pixel data of the video frame subjected to pixel correction according to the illumination adjustment coefficient. According to the method, the preliminary color mapping matrix is generated through the color histogram matching method, the color distribution of the image can be optimized, the image is enabled to better conform to the color features of the target reference frame, the color temperature adjustment is performed on the image according to the illumination adjustment coefficient and the illumination threshold, the cold and warm tones of the video are enabled to be accurately adjusted, and the image quality is improved. Through successively carrying out pixel correction, color correction and local area correction, a stable color adjustment frame is provided, so that the video styles are consistent.

The invention discloses a low-light image enhancement method based on a YUV color space, and relates to the technical field of image processing. According to the method, a color space illumination enhancement network (CSLNet is established, brightness (Y) and chrominance (U, V) information is separated, and brightness and chrominance channels are respectively subjected to targeted processing, so that the image brightness and details are improved, the problems of color distortion and noise amplification are avoided, the CSLNet makes full use of the advantages of a YUV color space, and the image quality is improved. Wherein the Y channel represents brightness information, the U and V channels represent chrominance information, brightness enhancement and color correction can be more accurately controlled through separation processing and the CSLNet, a color space enhancement module CSE Block (Color Space Enhancement Block) is introduced into the CSLNet, the CSE Block is fused with a U-shaped network structure and a multi-head self-attention mechanism, and optimization processing is specially carried out on chrominance channels U and V of a low-illumination image.

The invention discloses a marine litter intelligent identification and classification method based on a multispectral unmanned aerial vehicle remote sensing image, and relates to the technical field of image processing, and the method comprises the steps: collecting and preprocessing a remote sensing image of a preset sea area, constructing a color correction network, and carrying out the color correction of the preprocessed remote sensing image; multiband features and spectral indexes of enhanced garbage detection are obtained, and multispectral features are generated through multi-scale feature pyramid structure fusion; training a conditional generative adversarial network, inputting a remote sensing image and multispectral features, and performing pixel-by-pixel fusion on the enhanced remote sensing image and the remote sensing image after color correction to obtain a multiband fusion feature map; and constructing a lightweight detection network based on the OfficientDet-Lite, taking the multi-band fusion feature map as input, and outputting a bounding box and a garbage category of garbage by adopting a joint detection-classification architecture. According to the method, the problems of color distortion, contour fuzziness, small target missing detection and the like in marine litter identification are solved, and meanwhile, the real-time performance of marine litter identification and classification is ensured.

The invention discloses a cultural heritage digital reconstruction system based on deep learning, and the system comprises a data collection module, a data preprocessing module, a feature description module, a modeling module, and a model optimization module. The feature description module is used for acquiring data processed by the data preprocessing module, feature extraction is performed on point cloud data through a point cloud processing network in deep learning, the data preprocessing module is used for data preprocessing, the data preprocessing comprises correction, denoising, color correction and cutting, and the modeling module is used for modeling the point cloud data according to the extracted features. Three-dimensional reconstruction of the cultural heritage is carried out, and a model optimization module is used for smoothing a generated cultural heritage model, filling the deficiency and eliminating noise; the cultural heritage digital reconstruction method has the advantages that the digital system has rich details and sense of reality, people can conveniently visit and learn, and the efficiency and precision of cultural heritage digital reconstruction are improved.

The invention relates to the technical field of biomedical engineering and digital image processing, in particular to a digital image enhancement method based on cytopathology. The method comprises the following steps: eliminating noise and artifacts caused by non-uniform dyeing, section folding and dust factors; the problem of dyeing difference caused by different scanning devices is solved; key areas including cell nucleuses and cell membranes are highlighted, so that the visual effect of a low-contrast image is improved; segmenting an area, including cell nucleuses and cytoplasm, of the cells, and extracting morphological characteristics; the detail resolution of a low-resolution image, especially the definition of a small cell structure, is improved; the brightness component Y is corrected to compensate for the difference of different dyeing methods and dyeing qualities in brightness response, then the chromatic value is adjusted according to the color correction factor to make the colors of the images more consistent, and finally, the images which are acquired by different dyeing methods and dyeing qualities and subjected to brightness and chroma correction are fused to make the color of the images more consistent. And a final corrected image is obtained.

The invention relates to the technical field of underwater image enhancement, and provides a deepwater image enhancement method based on multi-color space coupling, which comprises the following steps: determining an optimal channel, a moderate attenuation channel and a severe attenuation channel in an RGB color space based on a pixel average value, carrying out asymmetric histogram cutting pre-correction on the optimal channel, and carrying out deep-water image enhancement on the optimal channel; designing a loss function to enable an attenuation channel to approach an optimal channel through iterative compensation; in the Lab color space, clustering and segmenting the brightness component L into a plurality of pixel blocks by using an unsupervised pixel clustering model PCM, and performing local contrast enhancement and guided filtering-based noise reduction processing on each pixel block; and finally, carrying out nonlinear stretching on the chrominance components a and b by adopting a self-adaptive exponential function to improve the color naturalness. Through a multi-color space cooperative enhancement mechanism, the limitation of single color space processing in a traditional method is effectively solved, and the color correction precision, brightness enhancement and noise reduction balance and the naturalness optimization effect of the deep water image are improved.

The invention discloses a tea leaf fermentation degree determination method with image recognition, which comprises the following steps: shooting tea leaves through a high-definition camera and a camera in different tea leaf processing stages according to different tea leaves to obtain high-definition images of the tea leaves, preprocessing the collected tea leaf images, and determining the fermentation degree of the tea leaves according to the preprocessed tea leaf images. Through denoising, contrast enhancement and color correction technologies, an acquired image is preprocessed, the image is converted into a proper color space for extracting color features in the tea image, and dominant hue and color distribution on the surface of tea are analyzed. The invention relates to the technical field of tea leaf detection, and the method can realize non-destructive, real-time and accurate tea leaf fermentation degree monitoring, and automatically judges the fermentation state of tea leaves by shooting tea leaf images and analyzing visual features of colors, forms and textures of the tea leaves in combination with a machine learning algorithm. Therefore, the automation level and the production efficiency of the tea processing process are improved.

The invention relates to the technical field of medical image processing, in particular to a digestive endoscopy image deblurring enhancement method and system.The method comprises the steps that firstly, an input digestive endoscopy original image is processed through a blurred region classification network, and a pixel-level blurred classification map capable of distinguishing an adhesion blurred region and a motion blurred region is generated; then, parallel processing is carried out according to the classification graph: for an adhesion fuzzy region, physical model restoration and color correction are carried out by estimating a transmissivity graph and an ambient light value; for a motion blur region, a self-adaptive non-blind deconvolution kernel is constructed to perform deconvolution sharpness. And finally, inputting the two processing results and the original clear area into a multi-scale feature fusion network together, carrying out adaptive feature weighted fusion and image reconstruction, and outputting a globally clear and detail-enhanced final image. According to the method, accurate identification and targeted enhancement of composite blurring are realized, and the visual quality and diagnosis availability of the digestive endoscopy image are effectively improved.

The invention discloses an underwater image enhancement method based on an improved MobileNetV4 (MobileNetV4). The method comprises the steps of firstly constructing a degraded underwater image data set and performing normalization preprocessing; a generator with MobileNetV4 as a trunk is provided, wavelet denoising (WD), color correction (CC), a multi-query attention mechanism (MQA) and a LayerScale layer are integrated, and high-frequency noise reduction, color temperature adjustment and feature stabilization are achieved; in cooperation with a multi-scale discriminator (MSD), Huber adversarial loss, Smooth L1 similarity loss and dual-channel content perception loss optimization training are adopted. Compared with a traditional method, the model is smaller in parameter quantity and calculation quantity, but better performance is achieved, the UIQM index is improved by more than 15%, the requirements for light weight and enhanced quality are remarkably balanced, and the method has practical value in the fields of ocean exploration, biological monitoring and the like.

The invention discloses a scanning method and system based on pre-scanning and dynamic optimization, a medium and equipment, and the method comprises the specific steps: a pre-scanning step: carrying out the preliminary scanning of a file through light sources with different wavelengths through a multispectral scanning technology, and obtaining image information containing visible light and invisible light wave bands; a pre-scanning analysis step: based on a deep learning image analysis algorithm, analyzing a preliminary scanning image in real time, and identifying a file type, a non-uniform illumination region and a potential scanning problem; a light supplement adjustment step: dynamically adjusting the brightness, angle and spectral range of a light supplement lamp according to the pre-scanning analysis result; a high-precision scanning step: performing high-precision formal scanning to obtain a scanning image; and a post-processing step: based on AI denoising, sharpening, color correction and semantic analysis, generating an optimized scanning file. According to the method, dynamic adaptation to different document features can be realized through setting of the pre-scanning analysis step and the light supplementing adjustment step, and the scanning quality and efficiency are remarkably improved.

The invention relates to the technical field of image processing, in particular to an underwater crack segmentation-oriented color correction and texture sharpening double-branch enhancement system, which is characterized in that a data set is used for training, reasoning and analysis, and underwater crack images are stored in the data set; the system comprises an input unit used for receiving an underwater crack image; the color correction network is used for carrying out color correction on the underwater crack image and eliminating the problems of color deviation and low contrast of an underwater environment; the texture enhancement network is used for performing texture enhancement on the image after color correction; and the output unit is used for outputting the image enhanced by the texture enhancement network, and aims to recover the color and texture features of the underwater crack image through the color correction network and the texture enhancement network so as to enhance the perceptibility of the semantic segmentation model to the underwater crack.

The invention relates to the technical field of image processing and cultural relic protection, in particular to a cultural relic image virtual restoration method based on texture reconstruction and color correction, and the method comprises the following steps: S1, obtaining a digital image of the surface of a to-be-restored cultural relic, and carrying out the denoising and brightness equalization processing of the digital image; s2, identifying and segmenting a damaged area in the image; s3, selecting an optimal sample block which is most matched with the structure of the region to be filled; s4, performing adaptive affine transformation on the optimal sample block to generate a reconstructed texture block; s5, carrying out linear transformation to obtain a repaired texture block after color correction; and S6, seamlessly embedding the repaired texture block after color correction into the damaged area. According to the method, the texture direction alignment and the color distribution correction are combined, so that the consistency of the damaged area and the surrounding image in structure and color is realized, and the naturalness and integrity of cultural relic image restoration are remarkably improved.

The invention provides a multi-scale pyramid weighted fusion underwater image enhancement method based on double prior, and the method comprises the steps: obtaining a degraded underwater image, and carrying out the global and local cooperation body color calibration of the degraded underwater image; decomposing the color correction image into a base layer, a detail layer and a noise layer by adopting a variational decomposition algorithm; performing spectral prior and transmissivity loss constraint on the base layer image to obtain a defogged image; fusing the detail layer image and the noise layer image to obtain a filtered image; performing enhancement processing on the filtered image by adopting a nonlinear mapping and contrast enhancement strategy to obtain an enhanced image; performing multi-level feature integration and reconstruction on the defogged image and the enhanced image by adopting a multi-scale pyramid adaptive weighted fusion method to obtain an underwater image with natural color and high visual definition; according to the method, the traditional image processing and variational optimization thought is combined to effectively correct the color deviation of the degraded underwater image, the image contrast and the detail definition are improved, and the visualization effect of the underwater image is improved.

The invention provides a peritoneal dialysis solution detection data intelligent analysis method based on visual identification, and the method comprises the steps: obtaining a to-be-identified test paper image, carrying out the test paper region segmentation and color correction of the test paper image, and carrying out the brightness equalization processing of a test paper detection region, and obtaining a processed test paper detection region; obtaining a standardized color feature vector of a test paper color development area from the test paper detection area; carrying out mapping matching on the standardized color feature vector and a standard colorimetric database to obtain a colorimetric matching value, carrying out color abnormality identification according to the colorimetric matching value, comparing the color abnormality with a preset abnormal value to obtain a test paper colorimetric analysis result, and carrying out self-adaptive correction on the test paper colorimetric analysis result with abnormality; and constructing a personalized colorimetric model according to the colorimetric matching value in combination with a historical colorimetric matching value, and performing health trend analysis and early warning.

The invention provides a multi-degradation type adaptive image enhancement method and device, and the method comprises the steps: obtaining a degraded image, extracting degradation features in the degraded image, and generating a degradation vector; dynamically adjusting a preset weight coefficient of the multi-scale feature extraction branch based on the degradation vector, and performing weighted fusion on the extracted multi-scale features according to the adjusted preset weight coefficient to obtain a fused feature; on the basis of the degradation vector and the fusion feature, generating a detail mask and a noise mask, and optimizing the fusion feature to obtain an optimized fusion feature; and mapping the optimized fusion feature into a preliminary enhanced image, performing color correction on the preliminary enhanced image based on color statistical information of the degraded image, and outputting a final enhanced image. According to the method and the device, through self-adaptive degradation perception and dynamic weight fusion, a single algorithm can adapt to various degradation coexistence scenes, and the limitation of traditional series processing is broken through.

The invention provides a projection transformation method for hexahedral panoramic data to spherical panoramic data, and the method achieves the seamless splicing of panoramic images of six surfaces of a cube and the mapping of the panoramic images to a spherical coordinate system through the determination of a direction vector, the normalization of the vector, the conversion of spherical coordinates, the projection to an equidistant histogram, and an illumination and color correction algorithm. The problems of splicing gaps, deformation, texture distortion and the like in a traditional method are solved. The method is not only widely applied to the fields of virtual reality, augmented reality, panoramic video production and the like, but also greatly expands the application scenes, including but not limited to panoramic roaming, streetscape roaming experience, integrated use of specific roaming plug-ins, seamless embedding on a map platform and the like, and has wide application prospects. Therefore, the display effect of the panoramic data and the immersive experience of the user are remarkably improved.

The invention relates to an underwater image quality improvement method based on adaptive color correction and contrast enhancement, which comprises the following steps: firstly, designing an adaptive correction strategy to carry out channel compensation on an underwater image to obtain an underwater image after color correction; a brightness channel is extracted, and a color interference layer is filtered out, so that global backscattered light is estimated; and gradient domain detail enhancement is carried out, defogging processing is carried out on the base layer of the underwater image after color correction, brightness adjustment is carried out on uneven illumination, and an enhanced image is obtained. According to the invention, by compensating the attenuation of the underwater environment to the image information, the color distribution balance of the three channels is realized, so that the color channels of the underwater image are naturally distributed; a plurality of prior knowledge of the back scattering light is fused, a Gaussian filter of an adaptive standard deviation is constructed, a color interference layer is separated, and the back scattering light can be accurately estimated without being interfered by a white object and a highlight area; therefore, multi-target-oriented contrast enhancement is realized to improve the overall visibility of the image.

The invention belongs to the technical field of deep learning, particularly relates to a two-stage blind image defogging method based on prior guide diffusion, and aims to solve the problem of distortion of a traditional decontamination method in a complex scene. Comprising the steps that a double-stage blind image defogging model is constructed, and the double-stage blind image defogging model comprises a first stage and a second stage; wherein in the first stage, physical modeling is carried out based on an improved atmospheric scattering model, the improved atmospheric scattering model is an enhanced atmospheric scattering model in which a light absorption coefficient is introduced, and a transmission image, a fogless reference image and atmospheric light parameters are output through the first stage; in the second stage, generation optimization is carried out based on a diffusion model, the transmission image, the fog-free reference image and the atmospheric light parameters output in the first stage are used as physical priori to be fused into the generation process of the diffusion model, and image defogging is carried out. In the second stage, self-adaptive difference fusion convolution is set, a fog domain multi-source fusion attention mechanism is set, and a pixel level and wavelet domain double-effect color correction strategy is adopted.

The invention relates to a driving early warning device, a driving early warning method and a vehicle, and relates to the technical field of intelligent traffic. The driving early warning device comprises a camera, a picture processing chip and an early warning analysis module, and the camera is connected with the picture processing chip through a high-speed data line; the picture processing chip is connected with the storage module through a high-speed data line and is connected with the early warning analysis module through an internal communication line; the camera is used for collecting a vehicle environment image of the vehicle in the driving process and sending the vehicle environment image to the image processing chip; the picture processing chip is used for executing optimization operation on the vehicle environment image, and the optimization operation comprises preprocessing operation and abnormal target enhancement operation; the preprocessing operation comprises at least one of image denoising, contrast enhancement and color correction; and the early warning analysis module is used for executing abnormity analysis based on the vehicle driving state data and the vehicle environment image subjected to the preprocessing operation.

The invention belongs to the field of garment processing, and particularly relates to a garment color fastness gradient detection method based on visual processing, which comprises the following steps: carrying out standardized color fastness test on a garment sample, and collecting corresponding picture samples and color fastness parameters according to a test time sequence; preprocessing the collected picture samples according to a test time sequence, wherein the preprocessing comprises denoising, enhancement and color correction of the picture samples; extracting color features and texture features of the preprocessed picture samples, performing gradient classification on various picture samples according to a test time sequence, and establishing a color feature data set, a texture feature data set and a color fastness parameter data set which are associated; through standardized testing and automatic image acquisition, hundreds of samples can be processed at a time, predicted values are directly output based on a trained model, physical testing steps are reduced, key areas such as necklines and cuffs are positioned through area segmentation, local area testing is rapidly carried out, and compared with global testing, the method has the advantages of being high in pertinence and accuracy, and missing detection can be avoided.

The invention discloses a target tracking method and device based on a rolling and pitching type holder, and the method comprises the steps: building a polynomial response model through adaptive color calibration, and achieving the dynamic color correction; an improved YOLOv8 model is combined with a bidirectional feature pyramid network to carry out multi-scale target detection, and template matching is assisted to improve a low-confidence-coefficient scene recognition rate; predicting the position and the speed of the target by using Kalman filtering to cope with a shielding condition; and high-precision smooth rotation of the holder is realized through quaternion attitude solution in combination with PID (Proportion Integration Differentiation) and FOC (Fiber Operating Control) double-loop cooperative control. The device adopts a rolling and pitching type holder structure and comprises a main controller, an angle sensor, a visual sensor and a magnetic encoder, the mechanical structure is simplified, the weight is reduced, and higher response speed and higher control precision are achieved. The target tracking accuracy, stability and anti-interference capability of the intelligent mobile platform in a complex environment are remarkably improved, and the method is suitable for various dynamic scenes such as aerial photography and industrial inspection.