1796 results about "Sample image" patented technology

The invention provides a desertification monitoring grading and vegetation extraction method and system, and relates to the field of desertification remote sensing monitoring and ecological assessment, and the method comprises the steps: selecting a multi-temporal satellite image and an unmanned aerial vehicle image which cover a full research region according to a preset condition, and carrying out the data preprocessing of the multi-temporal satellite image and the unmanned aerial vehicle image; the method comprises the following steps: constructing a desertification difference index by fitting a feature space of a vegetation index and a surface albedo by using a multi-temporal satellite image, grading the desertification degree of a research area to obtain a grading result, and locking a key monitoring area in the research area according to the grading result; a vegetation sample image of a key monitoring area is obtained from an unmanned aerial vehicle image, HSL color space conversion and hue optimization processing are carried out on the vegetation sample image, and a normalized vegetation index based on HSL is constructed, so that vegetation information of the key monitoring area is finely extracted, a key desertification disaster area is effectively positioned, and the accuracy of the desertification disaster area is improved. And vegetation information in the region is finely extracted.

The invention relates to a few-sample image classification method based on hyperbolic space image-text local feature alignment, and belongs to the technical field of image recognition and artificial intelligence, and the method comprises the steps: generating word-level attribute description for a support set image through employing a multi-mode large language model; encoding the image and the text by adopting a vision-language model; constructing a hyperbolic local feature alignment module in a hyperbolic space, screening most relevant image local features for text local features by calculating hyperbolic cosine similarity, and fusing by using hyperbolic weighted average; designing a hyperbolic cross attention module, and aggregating key information from the multi-modal local features of the support set to construct a category prototype by taking query image aggregation features as guidance; and finally performing classification based on the hyperbolic geodesic distance. According to the method, the hierarchical modeling capability of the hyperbolic space and the semantic priori knowledge of the large language model are fully utilized, fine-grained multi-modal feature alignment is realized, and the small sample image classification performance is improved.

The invention discloses a form category determination method and device for a power system, and the method comprises the following steps: obtaining different categories of form sample images, and extracting keywords and the positions of the keywords in the form sample images; constructing a relation direction matrix of the category form; obtaining a text block set of the to-be-classified form; constructing a relation direction matrix of the to-be-classified form based on the relative position relation between the text blocks; based on a similarity calculation scheme, calculating a similarity score between the relation direction matrix of the to-be-classified form and the relation direction matrix of each category of form; and thus, the category of the to-be-classified form is judged. According to the method, high robustness can be still kept when the forms shift or the fields change, and the classification accuracy of the forms with complex structures or sparse keywords is remarkably improved; meanwhile, the calculation amount is greatly reduced, a large-scale form library can be adapted, and the application requirement of an actual service scene of a power system is met.

A semantic segmentation model training method and apparatus, an electronic device and a storage medium are provided. The semantic segmentation model training method includes: acquiring a sample image, and extracting visual image features corresponding to the sample image by a semantic segmentation model to be trained; processing the sample image to obtain a text image feature corresponding to the sample image, the text image feature being an image feature generated from language description text for the sample image; fusing the visual image features with the text image feature to obtain multimodal features, and performing image segmentation prediction based on the multimodal features to obtain a target loss; and training the semantic segmentation model to be trained based on the target loss to obtain a target semantic segmentation model.

A semantic segmentation model training method and apparatus, an electronic device and a storage medium are provided. The semantic segmentation model training method includes: acquiring a teacher semantic segmentation model that is pre-trained, the teacher semantic segmentation model including a first teacher network and a second teacher network, the first teacher network having structural characteristics of low depth and high width, and the second teacher network having structural characteristics of high depth and low width; processing a sample image based on the teacher semantic segmentation model to obtain a first segmentation map and a second segmentation map; and training a student semantic segmentation model that is lightweight according to the sample image, the first segmentation map and the second segmentation map, so as to obtain a target semantic segmentation model.

The invention belongs to the related technical field of image processing, and discloses a texture surface defect generation method and system based on a mask perception image redrawing network, and the method comprises the steps: (1) obtaining a low-frequency structure feature based on a defect sample image of a to-be-processed product category, a binary mask for marking a defect position, and a convolutional coding module; (2) a context sliding window attention modeling module carries out long-range dependence modeling on the feature sequence, and attention calculation is carried out between effective feature marks to obtain high-frequency detail features; (3) inputting the low-frequency structural features and the high-frequency detail features into a collaborative feature fusion module for fusion to obtain fusion features; (4) a style modulation decoding module performs up-sampling on the fusion features based on the comprehensive style tensor; and (5) generating a defect image based on the trained generator network model, the defect-free sample image of the to-be-processed product category and a user-defined defect mask. According to the invention, the authenticity of defect generation is improved.

The invention discloses a multi-modal retrieval model training method and a multi-modal retrieval method and device, and relates to the field of computers, in particular to the field of artificial intelligence such as deep learning, large models and intelligent retrieval. According to the specific implementation scheme, a sample image and text label information corresponding to the sample image are obtained; according to the sample image and the text label information, generating a target main body mask image corresponding to the text label information by utilizing a visual main body detection model; performing feature extraction on the sample image by using the initial multi-modal retrieval model to obtain an image feature vector; and according to the target main body mask image and the image feature vector, training the initial multi-modal retrieval model to obtain a target multi-modal retrieval model.

The invention is suitable for the technical field of artificial intelligence, and provides an abnormal sample image generation method, electronic equipment and a storage medium, and the method comprises the steps: setting a material parameter library and a scene parameter library, and constructing a paired industrial defect sample data set in combination with three-dimensional geometric models of a plurality of sample workpieces; constructing category text description, defect text description and material category text description of various workpieces, and constructing and training an abnormal sample image generation model in combination with the paired industrial defect sample data set; obtaining a target normal image, a candidate defect area mask image, a target category text description, a target defect text description and a target material category text description of a target category workpiece, and inputting the target normal image, the candidate defect area mask image, the target category text description, the target defect text description and the target material category text description into an abnormal sample image generation model for processing to obtain a target defect image and a target defect area mask image; the training precision and flexibility of the abnormal sample image generation model are improved, and then the efficiency and precision of abnormal sample generation are improved.

The invention discloses an unmanned aerial vehicle electric power inspection image segmentation method and system, and relates to the technical field of image segmentation. By means of a labeled sample image set, positive and negative information is fully fused in a confidence map cooperation module to carry out confidence modeling, so that a cooperation confidence map can accurately reflect the difference between a target and a background; the negative sample distribution data effectively depicts the features of background interference, then high-discrimination positive and negative points are screened out in the point selection module, accurate prompt is provided for segmentation, iterative optimization is performed through the noise sensing and refining module, and therefore the segmentation efficiency is improved under the condition that only few labeled samples are needed. According to the method, high-precision, high-robustness and training-free segmentation of the target component in the electric power inspection image is realized, the influence of complex backgrounds and similar interferents is effectively resisted, the method has high robustness, the technical problems of low segmentation precision and insufficient robustness under the condition of few labeled samples in the prior art are fully solved, and the requirements of electric power inspection on automation and precision are met.

Disclosed in the present application are a compression and training method and apparatus for a defect detection model. The method comprises: obtaining, by means of segmentation labeling, a segmentation labeling factor matrix of each sample image; inputting each sample image into both a first defect detection model and a second defect detection model, and extracting first feature maps outputted by target convolutional layers in the first defect detection model and second feature maps outputted by corresponding target convolutional layers in the second defect detection model; and calculating, by using the segmentation labeling factor matrix, corrected distances between corresponding feature vectors of the first feature maps and the second feature maps, and calculating, as a first loss function, the sum of the corrected distances between all the feature vectors of the first feature maps and the second feature maps. The present embodiment can improve the accuracy of detecting tiny product appearance defects by means of a compressed defect detection model.

Provided in the present disclosure are a training method for an object detection model for a low-quality image, an object detection method for a low-quality image, and a related device. The training method comprises: acquiring a low-quality sample image for a target region; inputting the low-quality sample image into a first encoder to obtain an initial feature of the low-quality sample image; inputting the initial feature into a transfer convolutional network, and on the basis of a first target feature, training the transfer convolutional network to obtain a target convolutional network, wherein the first target feature is obtained on the basis of the initial feature; on the basis of the target convolutional network, obtaining a second target feature for the low-quality sample image; inputting the second target feature into a first detection head, and training the first detection head to obtain an object detection head, wherein the first detection head is obtained on the basis of the first target feature; and on the basis of the first encoder, the target convolutional network and the object detection head, obtaining an object detection model, wherein the object detection model is used for performing object detection on a low-quality image to be subjected to detection. The present disclosure provides technical support for realizing efficient object detection.

The invention relates to the technical field of image generation, and discloses a high-quality human body image generation method and device, and computer equipment. The method comprises the following steps: constructing a training data set, wherein the training data set comprises a sample image of a human body, a text description of a corresponding human body attribute, a human body analysis graph and a plurality of attribute tags; an image generation network is constructed, the image generation network comprises an encoder, a UNet and a decoder which are connected in sequence, the image generation network further comprises a time feature aggregation module and an attribute perception reward module, and the attribute perception reward module is used for predicting a target reward score and a prediction reward score; training an image generation network by minimizing the difference between the target reward score and the predicted reward score; and inputting the text description of the human body image to be processed and the human body analysis graph into the trained image generation network to obtain a target image. By adopting the method, the high-quality human body image with space alignment and consistent attributes can be generated.

Embodiments of this application disclose an image processing method and apparatus, a device, and a medium. The method includes: obtaining a target image to be processed; inputting the target image to a pre-trained image-text model, a model loss of the image-text model including an image loss, and the image loss being constructed according to a first sample image and a second sample image that is obtained by converting a first sample text configured for describing the first sample image; and obtaining a target text configured for describing the target image and generated by the image-text model. In technical solutions of the embodiments of this application, the generated target text can describe the target image as accurately as possible, thereby ensuring accuracy of the target text.

The invention discloses a carbon fiber prepreg appearance defect detection method and system based on deep learning, and relates to the technical field of image analysis, and the method comprises the steps: collecting and preprocessing a sample image of the surface of a carbon fiber prepreg; inputting the image into a pre-trained convolutional neural network feature extraction model, extracting middle / high-level features, performing up-sampling alignment, and performing fusion to form a multi-scale fusion feature map; constructing a feature memory library by using a fusion feature vector extracted from a defect-free sample through a convolutional neural network feature extraction model; obtaining a feature vector of a to-be-detected image according to the same process, performing similarity comparison with the memory bank, and calculating an abnormal score graph based on a similarity distance result; and generating an abnormal region mask image according to the feature distribution of the abnormal score image based on the abnormal judgment threshold value, and visually outputting a defect detection result. Through the technical scheme of the invention, the missed detection risk is reduced, defect large sample labeling is not needed, the cross-scene stability is improved, and the result expression is more intuitive.

The invention discloses an intelligent detection method and device for a cotton water miscellaneous micronaire value based on multi-source information fusion, and relates to the field of cotton quality detection.The method comprises the steps that the weight, the initial air pressure difference, the cotton sample image, the resistance value, the environment temperature and the environment humidity of a to-be-detected cotton sample are obtained; according to the initial air pressure difference and the weight, the compensated air pressure difference is determined through a weight compensation model; according to the cotton sample image, utilizing an image segmentation technology to extract the impurity area percentage and the impurity grain number of the cotton sample to be detected, and determining the impurity content of the cotton sample to be detected; according to the resistance value, the environment temperature and the environment humidity, utilizing a moisture regain prediction model to determine the moisture regain of the cotton sample to be detected; and determining the micronaire value of the cotton sample to be tested by using a Stacking ensemble learning model according to the compensated air pressure difference, impurity content and moisture regain. According to the method, the moisture regain, the impurity rate and the micronaire value of the cotton can be accurately detected in a non-standard detection environment.

The invention discloses a multi-target tracking method for an offshore rain, fog and jitter environment, and the method comprises the steps: obtaining multi-target image data in the offshore rain, fog and jitter environment, and carrying out the target cutting and fusion of the multi-target image data based on a target enhancement segmentation strategy, and obtaining an enhanced sample image; mapping the enhanced sample image and target features in the multi-target image data into a candidate frame set of a pixel scale, and obtaining a sample data set containing adaptive Anchors according to the candidate frame set based on a clustering algorithm; performing model training on the multi-target detection network through the sample data set to obtain an optimal detection model so as to realize target detection; and defining a target state vector and a target observation vector according to a detection result, and realizing multi-target tracking under the marine rain and fog and jitter environment based on an improved Kalman filtering algorithm. The problems that in the prior art, the precision of multi-target detection under the marine rain and fog and jittering environment is insufficient, and a systematic solution for multi-target tracking under the marine rain and fog environment and the jittering scene is lacked are solved.

The invention discloses a remote sensing rapid diffusion super-resolution reconstruction method based on adaptive scene perception, belongs to the technical field of mode recognition, and is used for super-resolution reconstruction of remote sensing images. The method comprises the following steps: preprocessing a high-resolution image, and calculating a residual error between the high-resolution image and a bicubic up-sampling result as a training target of a diffusion model; performing wavelet transformation on the bicubic up-sampling image through a frequency domain enhancement module, extracting frequency domain features, and fusing the frequency domain features with original spatial domain features to serve as condition input of a model; inputting the fused features into a conditional diffusion model, carrying out adaptive rapid sampling by adopting a linear-cosine hybrid scheduling strategy, and completing high-quality reconstruction within 20 steps; the feature expression capability is enhanced through an overlapping and crossing attention module integrated in the U-Net denoising network; and finally, fusing the residual error reconstruction module and the bicubic up-sampling image to obtain a final super-resolution reconstruction result. Experiments carried out on two common data sets (Potsdam and Toronto) show that the method of the invention significantly improves the sampling efficiency while maintaining the reconstruction quality, and is superior to an existing remote sensing image super-resolution method.

A fingerprint information processing apparatus includes: an output unit that outputs a certainty factor that is an index indicating probability in which fingerprints indicated by a fingerprint image correspond to at least one of a plurality of pattern types, by using the fingerprint image and a learning model constructed by machine learning using learning data including a sample image indicating fingerprints; and a processing unit that performs processing based on the certainty factor.

The present application provides an information processing device and method, and a computer-readable storage medium. The information processing device comprises a processing circuit which is configured to: generate a saliency map of a sample image on the basis of a predetermined model which processes a task for the sample image, wherein the saliency map reflects the degree of attention to objects at different positions in the sample image when the predetermined model processes the task; and adjust, on the basis of the saliency map and the labeling area in the sample image, the parameters of the image signal processor which generates the sample image, such that the difference between the task processing result and the labeling value of the sample image meets a preset condition.

The invention relates to a mapping system and method for flight inspection of indoor equipment, the system comprises a data acquisition end and an offline data processing end, the data acquisition end comprises an unmanned aerial vehicle and a sensing module, and the sensing module comprises a laser radar module, a binocular camera module, an inertial measurement module and an airborne recording module; the method comprises the following steps: synchronously acquiring multi-source data through layered flight; performing space-time alignment and preprocessing; self-adaptive sampling is carried out based on point cloud geometric prior guide image features, and laser-vision joint features are generated; fusing point cloud registration, joint features and inertial data, and carrying out joint estimation on adjacent frame pose increments; performing loopback detection based on the key frame to generate a closed-loop constraint; constructing and optimizing a pose map to eliminate cumulative drift; and finally fusing to generate a global point cloud map. According to the method, the space consistency and geometric accuracy of the map can be effectively improved, and a reliable data basis is provided for digital modeling and intelligent operation and maintenance of indoor equipment inspection.

The invention belongs to the field of image processing and computer vision in computer intelligent information processing, and discloses an industrial product unsupervised anomaly detection method and system based on a conditional control diffusion model, and the method comprises the steps: constructing a diffusion model based on conditional control and a multi-scale double-attention mechanism, reconstructing the to-be-detected sample image by using a diffusion model based on condition control and a multi-scale double-attention mechanism to obtain a reconstructed image with the same size as the to-be-detected sample image; and extracting feature maps of different scales from the to-be-detected sample image and the reconstructed image through the same pre-trained feature extraction model. Performing a two-stage background denoising algorithm on each scale feature map to obtain an initial abnormal feature map; calculating an average value of a plurality of initial abnormal feature maps obtained by using the same method to obtain a final abnormal feature map; and processing the final abnormal feature map by adopting a thermodynamic diagram generation algorithm to generate a final abnormal thermodynamic diagram, thereby realizing the anomaly detection of the to-be-detected sample image.

The invention provides a medical magnetic resonance image reconstruction method and system, and belongs to the technical field of image reconstruction, and the method comprises the steps: obtaining sampling coding and under-sampling k space data; processing the under-sampled k space data through inverse Fourier transform, and converting the k space data into an image domain to obtain under-sampled image data with blurring or artifacts; carrying out preliminary restoration on the undersampled image data based on sampling coding, further applying data consistency operation on the image to obtain a rough image, and taking the rough image as an image condition vector after underspace coding; acquiring a text serving as a cue word, inputting the text into a text encoder, and encoding the text into a high-dimensional semantic embedding vector by the text encoder; and inputting the high-dimensional semantic embedding vector and the image condition vector into a reconstruction model based on the correction flow to predict a full-sampling MRI image, and generating a final reconstructed MRI image.

The invention relates to a urine specific protein detection sample quality screening system and screening method. The screening method comprises the following steps: firstly, creating a urine specific protein quality model; then collecting an image of a to-be-detected sample; and finally, analyzing and evaluating the sample to be detected according to the sample quality model and the image of the sample to be detected, and converting urine colors (such as light yellow, deep yellow, hematuria and the like) into quantifiable numerical values by collecting the image of the sample (urine). Compared with traditional naked eye observation, judgment deviation caused by subjective factors such as experience and ambient light of detection personnel is avoided, and the detection result is more objective and repeatable; according to the method, the sample image is converted into quantifiable numerical values so as to carry out numerical analysis on the detection data of the color, clarity and turbidity of the sample, hematuria and urine with high turbidity in the sample are automatically screened out, manual operation steps are reduced, the detection efficiency is improved, and the method is particularly suitable for clinical or large-scale screening scenes.

A generation method includes specifying a first distribution of postures of a person and a second distribution of positions or orientations or both of a camera based on a plurality of sample images in which the postures of the person and the positions and orientations of the camera that captures the person are different from each other augmenting the postures of the person in a range included in the first distribution augmenting the positions or orientations or both of the camera in a range included in the second distribution and generating an augmented image based on the augmented positions or orientations or both of the camera and the augmented postures of the person, by using a processor.

The invention discloses an automobile wire harness quality inspection method based on visual inspection, and belongs to the field of visual inspection and automobile wire harness detection.The method comprises the steps that a normal sample image set is constructed based on visual images of qualified automobile wire harnesses; self-adaptive preprocessing is carried out; inputting a pre-trained visual attention network, extracting key visual features of qualified wire harnesses, and constructing a normal feature library; collecting a visual image of a to-be-detected wire harness, performing adaptive preprocessing, and extracting key visual features of the to-be-detected image; calculating the feature deviation degree between the key visual features of the to-be-detected image and the normal feature library, and judging whether defects exist or not through the feature deviation degree; dynamically updating a normal feature library and a judgment threshold value through an online self-calibration module; and for the to-be-detected image which is judged to have the defect, positioning a defect area through a visual attention thermodynamic diagram, matching a preset defect feature template library, determining a defect type and outputting a quality inspection conclusion. According to the invention, appearance and assembly precision defects can be covered.

The invention discloses a defect detection sample data generation method and related equipment, and relates to the technical field of defect detection sample data generation, and the defect detection sample data generation method comprises the steps: obtaining a good product background image and an expected defect direction; based on the expected defect direction, respectively generating a structure guide mask and a semantic guide text cue word according to the defect knowledge base; inputting the non-defective product background image, the structure guiding mask and the semantic guiding text cue word into a local redrawing model, synthesizing a local defect image, and determining defect labeling information of the local defect image according to the structure guiding mask; and integrating the local defect image and the defect labeling information, and outputting defect detection sample data. According to the method, the structure guide mask and the semantic guide text cue word are generated through the defect knowledge base and are input into the local redrawing model to synthesize the local defect image, so that the defect image is effectively generated, and finally, the beneficial effect of improving the accuracy when the defect sample image is used for defect detection is achieved.

The invention relates to the technical field of intelligent operation and maintenance of an electric power system, in particular to an inspection sample image data enhancement method for an electric power artificial intelligence platform, which is used for solving the problems that in the prior art, history and equipment knowledge cannot be fused to construct a forbidden area, a co-occurrence rule and component association, cross-component defect positions cannot be effectively adjusted, and the detection accuracy is poor. Defect distribution is difficult to accurately control, and physical rationality and engineering credibility are reduced. According to the method, a forbidden area, a co-occurrence rule and component association are constructed by fusing history and equipment knowledge, masks are generated through kernel density estimation to suppress invalid defects, co-occurrence frequencies are counted based on feature vectors, co-occurrence relationships are determined by combining distances and similarities, and the masks, matrixes and graphs are embedded and coded into conditional vectors, so that the non-ineffective defects are suppressed. And zero setting is performed on a forbidden area in the generative network, illegal co-occurrence is filtered, and cross-component defect positions are adjusted, so that defect distribution is accurately controlled, and physical rationality and engineering credibility are enhanced.

The invention belongs to the technical field of image processing, and discloses a rice bud stage drought resistance prediction method, which comprises the following steps: acquiring sample images of a plurality of individuals of a rice variety to be detected under control and stress conditions, performing foreground segmentation and structural region marking on the images, and identifying seed, bud and root regions. And multi-dimensional phenotypic features are extracted for each region to construct a single-sample high-dimensional feature vector. And carrying out group feature extraction on a plurality of single sample vectors under the same processing condition, and carrying out double correction on a sample confidence coefficient weight and a feature steady-state weight to obtain a group feature vector. And constructing a multi-dimensional drought resistance response feature vector according to the difference between the group feature vectors of the stress group and the control group, and inputting the multi-dimensional drought resistance response feature vector into a pre-trained classification model to obtain a drought resistance level prediction result. The method achieves the automation of drought resistance evaluation, effectively inhibits the interference of abnormal samples, and improves the evaluation accuracy.

The invention belongs to the technical field of image processing, and discloses a microscopic imaging depth-of-field fusion method and device, equipment and a storage medium, a plurality of sample images in different depth-of-field scenes are collected, each sample image comprises a focus area, and the focus areas in different sample images correspond to different depth-of-field scenes; each sample image is subjected to definition scoring to obtain a definition score, image blocks are cut from each sample image, the image blocks of different sample images have the same image position and size, and target weight coefficients of the image blocks in each sample image are calculated according to the definition scores; the image blocks of the plurality of sample images are calculated and synthesized to obtain the target image, and the target image comprises all different focus areas, so that the target image has relatively high depth of field, the overall definition of microscopic imaging can be improved, and subsequent image analysis and processing are facilitated; the whole process is automatically controlled, and the working efficiency can be greatly improved.

The invention provides a training method, a server, equipment, a medium and a product, and the method comprises the steps: obtaining the key information of a plurality of teacher models trained by a first server from the first server, and the training data of each teacher model comprises a first group of wafer sample images, training a first pre-trained student model based on the key information of each teacher model and each second group of wafer sample images to obtain each candidate student model, determining a target momentum parameter based on the performance index of each candidate student model and the momentum parameters of the plurality of teacher models, and obtaining a target momentum parameter of the candidate student model based on the candidate student model corresponding to the target momentum parameter. And determining a wafer defect detection model. The wafer defect detection model trained by the method can give full play to the potential adaptive ability, generalization ability, high availability and high reliability of AI, thereby ensuring that the model can adapt to the requirements of the semiconductor manufacturing industry.