222 results about "Cropping" patented technology

Since defect areas in collected hydro turbine defect images account for a small proportion and there is a significant color difference between surrounding background and defects, which creates too much redundant information and greatly affects detection speed, a method for detecting defects in a hydro turbine top cover based on an improved YOLOv8 model involves first cropping collected defect images to obtain cropped images, then expanding the cropped images by image enhancement techniques such as image flipping and image blurring to obtain a training dataset. A defect detection network based on YOLOv8-CBAM is constructed and trained to generate a defect detection model for detecting the defects in the hydro turbine top cover. The method achieves high precision in defect detection for the hydro turbine top cover.

Techniques are generally described for cropped image evaluation. In various examples, first image data representing a first image and second image data representing a cropped version of the first image may be received. An image captioning model may be used to generate first text data describing the first image data and second text data describing the second image data. A first encoder may be used to generate first data representing the first text data and second data representing the second text data. In various examples, a third data representing a degree of similarity between the first data and the second data may be generated. In some cases, first computer-executable instructions configured to cause the second image data to be displayed on a display may be generated based at least in part on the third data.

The invention relates to the technical field of crop growth monitoring, in particular to a method and system for monitoring crop growth through images, and the method comprises the following steps: deploying an unmanned aerial vehicle, flying over a target paddy field, collecting image data, transmitting the image data to a ground station in real time through data transmission, and generating a preliminary image data set; and cutting and correcting the initial image data set. According to the invention, the unmanned aerial vehicle is used for image acquisition, the problem that the visual angle of a fixed ground camera is limited is avoided, and the data hysteresis is reduced through real-time data transmission, so that the monitoring is more timely. In the image data cutting and correcting process, angle deviation and background noise are eliminated, and the leaf area index calculation accuracy is improved. Through introduction of time sequence analysis, dynamic monitoring of crop growth trend is realized, growth evaluation is no longer limited to a single time point, historical data comparison is formed, and the prediction capability of the growth state is improved.

The invention relates to the technical field of textile printing and dyeing, in particular to a setting machine cropping moisture content control method and system based on MPC. An MPC model is constructed, and the MPC model comprises the steps that actually-measured and preset water content and the first control parameter state vector serve as input, and predicted water content is obtained; minimizing the difference value between the predicted water content and the preset water content as a target; solving and optimizing the control increment by using the optimization matrix and sequential quadratic programming; and performing action regulation and control on an execution mechanism by utilizing the optimal control increment. The invention solves the problems of insufficient detection precision, hysteresis and poor interference resistance in the existing single temperature feedback control of the moisture content of the fabric.

The invention relates to the technical field of intelligent agriculture, in particular to a planting management method and system for intelligent agriculture, and the method comprises the steps: S1, obtaining multi-modal data through configuring an independent sensing module and a local control execution end for each micro-block; s2, recognizing a current crop variety in real time according to the multi-modal data by adopting a low-power-consumption edge computing device in combination with image recognition; when the system is used, each micro-block operates independently, maintenance of each plant can be refined, multi-source sensing data can be fused, the problems of data lag and weight deviation among different sensors are solved, full-automatic feedback regulation and control are achieved, the labor burden of workers is reduced, low-power-consumption and high-real-time recognition is facilitated, multi-source data such as images and fusion of temperature and humidity are achieved, and the system is suitable for popularization and application. According to the method, the identification accuracy is improved, the problem of crop confusion which is like a long variety can be solved conveniently, accurate management in multi-variety mixed cropping planting can be achieved conveniently, and the accuracy of fine management and control of different demands of multiple different crops in a small range can be improved.

The invention relates to a projector automatic obstacle avoidance method and device combining adaptive picture cutting and deep learning, and the method comprises the steps: enabling a projector to project a white background picture, capturing a projection picture through a camera, calculating a difference image based on a projection region background image and a projection picture image, carrying out the adaptive cutting of the difference image, and obtaining a projection region image, the difference image can highlight the difference between the projection area and the background area, the projection area image can be found out more conveniently, then a deep learning model is used for detecting an obstacle in the cut image and recording position information, the maximum projection area is calculated, the position information of an obstacle-free area is obtained and then converted to a projector coordinate system, and the position information of the obstacle-free area is obtained. And finally projecting a final picture. Therefore, the automatic obstacle avoidance of the projector is realized, the clear and complete projection picture is ensured, the problem of poor projection effect in a complex environment due to traditional manual processing and the prior art can be effectively solved, and the projection quality of the projector in a complex scene is improved.

The invention discloses a transformer substation bird nest hidden danger identification method and system based on multi-model collaboration, and belongs to the technical field of computers, and the method comprises the steps: carrying out the image collection in a transformer substation scene through employing unmanned plane equipment, and obtaining a transformer substation bird nest hidden danger image set; inputting the substation bird nest hidden danger image set into a target area identification model for potential target identification and image clipping, and outputting a to-be-detected image; based on a preset recognition task, performing visual feature enhancement on the to-be-detected image, and generating a prompt word text; inputting the to-be-detected image and the cue word text into an image analysis model for bird nest recognition to obtain a recognition result; and outputting a bird nest position and an alarm signal in the to-be-detected image according to the identification result. Therefore, by implementing the method and the device, the problem that non-nest objects with similar colors and forms cannot be effectively distinguished in a complex environment in the prior art, so that the false recognition rate of hidden danger recognition of the transformer substation is high can be solved.

To solve the problem of saving time and labor in a system for automatically cutting out and re-editing broadcast highlights, and making it easy to create attractive highlight videos.SOLUTION: A system for automatically cutting out and re-editing broadcast highlights uses AI to automatically extract exciting scenes, generate subtitles, and add sound effects.SELECTED DRAWING: Figure 1

The invention provides an automatic image cutting method and system based on face key points, which are applied to the technical field of image processing, and the method comprises the steps: inputting a portrait image into a portrait segmentation model for foreground extraction, and obtaining a figure region mask; performing top positioning estimation based on the figure area mask to obtain a top position of the portrait image; inputting the portrait image into a face key point detection model to obtain a plurality of key points; determining a face midpoint of the portrait image based on the position relationship of the plurality of key points; performing affine transformation estimation based on the source point set and a preset target point set to obtain an affine transformation matrix; adjusting the vertical offset of the affine transformation matrix to obtain a longitudinal correction matrix; performing edge correction on the longitudinal correction matrix to obtain a cutting transformation matrix; and performing image transformation on the portrait image based on the cutting transformation matrix to obtain a standard composition image. According to the invention, the cut image with uniform size, standard composition and good detail retention can be generated.

The embodiment of the invention provides a target detection method and a related device, and the method comprises the steps: carrying out the cutting of an image based on a cutting overlapping rate and a cutting size, and obtaining a plurality of sub-images; detecting a to-be-detected target in the plurality of sub-images to obtain a target detection result of the plurality of sub-images; restoring the target detection nodes of the plurality of sub-images to corresponding positions of the image to obtain a target detection result of the image; processing the target detection result based on the regional position of the target detection result in the sub-image to obtain a processed target detection result of the image; the region position in the sub-image comprises a reserved region, a filtering region and a non-maximum suppression region which are determined in the sub-image based on the clipping overlapping ratio. According to the embodiment of the invention, corresponding processing can be carried out on the target detection results of different areas, the data volume needing complex image processing is reduced, and the target detection efficiency can be effectively improved.

The invention belongs to the field of image processing, particularly relates to an automatic association adjustment method for ground identification lines based on block division, and aims to solve the problems of long updating period, potential errors and omission, high resource consumption and low efficiency. The method comprises the following steps: analyzing airport AIP vector data to generate a structured data set; and constructing a topological sub-region in each functional region according to the connection relation of the marked line end points, subdividing the grid units when the marked line density exceeds a threshold value, and outputting a layered region map. And taking the area / grid unit as a detection unit, detecting new and old AIP change areas and position identifiers, and generating new version change area data. Recursively searching an N-degree correlation line by taking the change area as a starting point, and only updating the correlation line of geometric, type or topological connection change. Finally, the changed data and the unchanged data are spliced, and a complete airport model is generated through boundary adaptive extension cutting and global topology reconstruction. According to the method, efficient and accurate updating of the airport ground identifier is realized.

A method for monitoring the grassland vegetation coverage based on continuous photography, comprising: determining an observation quadrat and continuously taking multiple vegetation photos of the vegetation within the observation quadrat throughout the entire growing season; cropping and geometrically correcting the multiple vegetation photos; obtaining the vegetation pixel ratio corresponding to the vegetation photos and comparing the vegetation pixel ratio with a preset second threshold; when the vegetation pixel ratio is greater than the second threshold, calculating the vegetation coverage corresponding to the vegetation photos by using the Otsu algorithm; otherwise, using the vegetation pixel ratio as the vegetation coverage; obtaining a dynamic change curve of the vegetation coverage based on the vegetation coverage corresponding to each vegetation photo. The present invention effectively improves the monitoring accuracy and efficiency of the dynamic change characteristics of the grassland vegetation coverage by combining continuous photography and the Otsu algorithm.

The invention discloses a paper-cut image generation method and system based on Stable Diffusion, a medium and equipment. The system comprises a data acquisition module, a cutting and marking module, a model training module, a model deployment module, a screen module and an adjustment module which are in communication connection. The method comprises the steps of obtaining a paper-cut image data set; cutting and marking each picture in the paper-cut pattern data set in sequence; training a text and graph generation network model by using the cut and marked paper-cut picture data set to obtain a trained paper-cut image generation network model; the trained paper-cut image generation network model is deployed to ConfyUI workflow, and the ConfyUI workflow is utilized to generate the target paper-cut image according to the text description, so that the paper-cut design efficiency can be improved, the design threshold is reduced, and the inheritance and innovative development of non-abandoned paper-cut art are promoted.

The invention discloses a cross-modal anti-attack method and system based on mask weight and random cutting, and belongs to the technical field of deep learning. The method comprises the following steps: extracting a saliency mask of a source image; disturbing the source image by using the disturbance variable to generate an adversarial sample; randomly sampling K cutting frames in combination with a significance mask; respectively extracting clipping characteristics of the clipping frame on the adversarial sample and target embedding selected on the target image according to an alignment mode; calculating the cutting loss of the cutting frame according to the cutting feature and the target embedding; calculating the weighted sum of the cutting loss according to the significance mask to obtain the total loss; performing back propagation according to the total loss so as to update the disturbance variable; and re-perturbing the source image by using the perturbation variable based on the updated perturbation variable until the maximum step number or total loss convergence is achieved. The method gives consideration to attack success rate, mobility and disturbance concealment, and can adapt to quality evaluation of different large language models.

The invention relates to the technical field of industrial detection, and provides a steel ladle air brick defect detection method and system based on image recognition, and the method comprises the steps: carrying out the preprocessing of a target steel ladle air brick image shot by an industrial camera, including edge feature extraction, cutting and enhancement, and obtaining a preprocessed image set; for the block corresponding to each preprocessed image, acquiring a multi-angle image set with different resolutions shot by a multi-angle sensor; inputting the multi-angle images into a defect feature extraction network to generate a feature image set, and integrating to obtain an integrated feature image; generating defect positioning and type information based on the integrated feature map and the defect identification model set; and finally, combining the initial image, the model set and the defect information to generate a defect detection result. According to the method, the defect detection precision is improved through multi-angle image fusion and model collaborative verification. According to the invention, the accuracy and reliability of steel ladle air brick defect detection can be improved, and the accuracy of defect positioning and classification is enhanced.

The invention discloses a double cropping rice classification method and system based on multi-source remote sensing data and improved indexes, and belongs to the technical field of remote sensing image recognition. The method comprises the following steps: firstly, acquiring multi-source remote sensing data in a whole double cropping rice planting time period, and based on the preprocessed multi-source remote sensing data, respectively extracting improved double cropping rice index features and spectral band features from each optical image, extracting texture features from a Sentinel-2 optical image, extracting topographic features from DEM data, and calculating the multi-source remote sensing data; the method comprises the following steps: extracting polarization characteristics from Sentinel-1 synthetic aperture radar data; and finally, inputting all the extracted features into a trained random forest model, and outputting a double cropping rice spatial distribution diagram. By combining the multi-source data, the problems that the observation density of a single data source is too low and the data quality is unstable are solved. The phenological index of the double cropping rice developed by the invention can accurately capture phenological characteristics of the double cropping rice, and the identification accuracy can be further improved in combination with other auxiliary characteristics.

The invention discloses a video or sequence image-based ponding detection and early warning method, which aims at the limitation of a traditional ponding detection technology, utilizes popularization equipment such as a smart phone and an automobile data recorder to shoot a ponding area image, and performs format conversion, image preprocessing, ponding area range cutting and elevation model cutting to obtain a ponding detection result. Related steps are repeated under the waterlogging-free condition to obtain comparison data, and finally the waterlogging depth is calculated and an early warning system is established. The method has the advantages that the cost is reduced by popularizing equipment, the method is suitable for various environmental terrains, real-time monitoring can be achieved, data acquisition is flexible, processing and analysis are convenient, the visualization degree is high, sustainable monitoring is supported, operation is easy, the potential of being integrated with other technologies is achieved, accumulated water detection and early warning efficiency and accuracy can be effectively improved, and the method is suitable for popularization and application. And powerful support is provided for disaster prevention and reduction.

A system for recognizing vertically oriented alphanumeric text in images, the system including a processor configured to receive one or more images comprising vertically oriented alphanumeric text and detect one or more regions-of-interest in each image via a trained text detector. The processor is configured to execute a cropping of the detected one or more regions-of-interest encompassing vertically oriented alphanumeric text from each image to obtain one or more text crop portions and rotate the one or more text crop portions to obtain one or more orthogonally rotated text crop portions. The processor is configured to execute a trained ensemble of two different text recognition models on each of the obtained one or more text crop portions and the one or more orthogonally rotated text crop portions and generate a set of candidate recognized text strings based on the executed trained ensemble and determine a final recognized text string.

The present invention relates to an image raindrop removal method based on the guidance of the reverse rain removal process. The method comprises the following steps: step S1, performing data preprocessing, including data pairing, data random cropping, and data enhancement processing, to obtain a training data set; step S2, designing a multiple attention module, using pixel attention, channel attention, and spatial attention to extract image features; step S3, designing a U-shaped encoder-decoder network based on the multiple attention module, which is used to extract features of the image at different scales; step S4, designing an image feature fusion module, which fuses the image features and the original image to obtain a fused restored image; step S5, designing a forward raindrop removal network and a reverse raindrop removal network, and using the reverse raindrop removal network to guide the training of the forward raindrop removal network; step S6, inputting the attached raindrop image into the trained forward raindrop removal network, and outputting a clean image with raindrops removed.

The present invention discloses a method for extracting two-dimensional pore structures of engineered wood materials based on an autoencoder. The method includes the following steps: obtaining SEM images of engineered wood materials; performing cropping and image enhancement processing on the SEM images; designing an autoencoder network model including an encoder and a decoder, and inputting the processed SEM images into the autoencoder network model for training; using the trained autoencoder network model to perform binary extraction on the processed images, and the two-dimensional pore structures of the engineered wood materials can be obtained. The present invention uses an unsupervised autoencoder network model to perform binary extraction of the pore structures of the SEM images of engineered wood materials, which has the characteristics of high robustness and high precision, and can avoid noise and pseudo-pores of traditional threshold methods at the same time.

The invention provides a display device and a picture cutting method. After the display device enters the cutting mode in response to the cutting instruction, when the size of the first original image is larger than the size of the display area, the first original image can be zoomed according to the first zooming proportion to obtain the second image, and the first preview image corresponding to the cutting area is previewed in real time in the cutting process with the second image as the preview basis. When the user confirms cutting, the first preview image can be mapped to the first sub-original image in the first original image based on the first scaling, and the content consistency of the first sub-original image and the first preview image is ensured. And cutting out a first sub-original image from the first original image, and displaying the first sub-original image in the display area after zooming processing according to the first zooming proportion so as to ensure that the display effect of the zoomed first sub-original image is consistent with the display effect of the first preview image.

The application discloses a target detection domain adaptation method based on background and foreground cropping and interchanging. The application adopts a teacher-student model framework of semi-supervised learning, slowly updates through an exponential moving average of a teacher model, enables the model to obtain information from main knowledge learned by a student model, and thus realizes a self-supervision effect. A domain discriminator is introduced to perform adversarial training, enhances the ability of the model to extract domain-invariant representation, and thus improves robustness. In addition, a foreground-background mixed instance strategy further enhances the domain adaptation capability of the model, effectively creates target domain instance images with source domain backgrounds and source domain instance images with target domain backgrounds through cropping, padding and pasting operations. Finally, an iterative optimization strategy is adopted to continuously improve the model performance, a student model is updated through gradient back propagation by calculating multiple loss functions, and a teacher model is updated using an exponential moving average.

The invention provides a leaf image mode classification method and system for double cropping rice spike fertilization, and the method comprises the steps: collecting a first leaf image parameter array and a second leaf image parameter array at a preset time window of target double cropping rice and at a plurality of positions of a first leaf and a second leaf; carrying out image parameter discrete analysis on the parameter arrays to obtain a first dispersion and a second dispersion, and calculating to obtain a leaf pollution degree by combining a preset dispersion; and carrying out averaging processing and leaf pattern classification on the parameter array, and respectively obtaining two leaf pattern arrays based on the image parameters and the pollution degree, thereby determining a proper leaf pattern and carrying out double cropping rice panicle fertilizer application. The technical problem that in the prior art, due to the fact that the test data amount is insufficient, the fertilization recommendation accuracy on different field parcels is not high is solved, and the technical effects that through analysis of the double cropping rice leaf image parameters and the mode classification of the pollution degree, accurate regulation and control of the panicle fertilizer are achieved, and the fertilization recommendation accuracy is improved are achieved.

The invention provides an electronic image stabilization control method, a program product, electronic equipment and a storage medium, and the method comprises the steps: converting a current frame image based on a homography matrix corresponding to the current frame image, and obtaining a first image region; determining whether a preset cutting area in the current frame image is completely located in the first image area or not; in response to the fact that the preset cutting area is not completely located in the first image area, adjusting the homography matrix; and carrying out conversion again based on the adjusted homography matrix until the preset cutting area is completely located in the first image area, and carrying out cutting based on the preset cutting area to obtain a stable image corresponding to the current frame image.

The present invention provides a method and apparatus for detecting vehicle damage, the method comprising the steps of: dividing the overall appearance of the target vehicle into N predefined blocks; performing image acquisition for each of the N blocks according to a predefined image acquisition model to obtain N original images corresponding to the N blocks; performing vehicle part detection for each of the N original images to obtain a vehicle part position detection result; cropping each of the N original images into M sub-images of a predefined size according to a predefined cropping model; performing damage detection for each of the N original images and its corresponding M sub-images to obtain a damage detection result; and fusing the vehicle part position detection result with the damage detection result to obtain a vehicle part damage result for the target vehicle.A standardized image acquisition and image preprocessing process reduces the number of images taken, thus increasing the speed and efficiency of the entire damage detection process.

The application discloses a kind of provincial range rural black and odorous water body satellite remote sensing identification method and system, it is related to environmental remote sensing technical field;A kind of provincial range rural black and odorous water body satellite remote sensing identification method, comprising the following steps: obtaining data, data processing, data image inlaying, obtaining water body distribution data, data cropping, obtaining index data product, obtaining classification grid, preliminary obtaining black and odorous water body distribution, obtaining the final distribution of black and odorous water body;The present application utilizes multi-source satellite remote sensing image data, in combination with water quality parameters, constructs the remote sensing identification model of provincial range rural area water body using NIR, NDWI, SWI index and slope data etc., then uses BOI, NDBWI etc. Index constructs rural area suspected black and odorous water body extraction model, in combination with the relevant data of water body field measurement in monitoring area determines the extraction threshold of water body and black and odorous water body, realizes the automatic identification and extraction of rural area black and odorous water body, and develops relevant software system.

The invention discloses a deep learning rice planting area extraction method fusing remote sensing image time-space spectrum information. The method comprises the following steps: step 1, constructing a time sequence of Sentinel-2 image spectral features and texture features; step 2, establishing an image set fusing spectrum-time-space features; step 3, constructing a CTH-Net hybrid deep learning model; the CTH-Net hybrid deep learning model comprises a CNN branch, a Transform branch, a TSSF module, a residual module, a Dropout regularization layer and a full connection layer; by optimizing a spectrum-time-space multi-dimensional data fusion method, efficient integration of remote sensing image spectrum, time and space features is realized. A space-time spectrum fusion (TSSF) module and a residual module are introduced into the CTH-Net, so that the capability of the model for extracting rice in a complex landscape is remarkably improved. The results show that the CTH-Net reaches 99.69% in the aspects of accurry, precision, recall and F1score extracted from the rice, and the CTH-Net is stable in different categories such as single cropping rice, double cropping rice, abandoned land and the like.

In a video capture system, a virtual lens is simulated when applying a crop or zoom effect to an input video. An input video frame is received from the input video that has a first field of view and an input lens distortion. A selection of a sub-frame representing a portion of the input video frame is obtained that has a second field of view smaller than the first field of view. The sub-frame is processed to remap the input lens distortion to a desired lens distortion in the sub-frame. The processed sub-frame is output.

The present invention relates to a method and device for automatic obstacle avoidance for a projector that combines adaptive cropping of images with deep learning. First, the projector projects a white background image, then uses a camera to capture the projected image. A differential image is calculated based on the background image of the projection area and the image of the projection screen. The differential image is adaptively cropped to obtain the projection area image. The differential image can highlight the difference between the projection area and the background area, making it easier to find the projection area image. A deep learning model is then used to detect obstacles in the cropped image and record their position information. The maximum projected area is calculated, and the position information of the obstacle-free area is obtained. This information is then converted to the projector coordinate system, and the final image is projected. This method achieves automatic obstacle avoidance for the projector, ensuring a clear and complete projection image. It can effectively solve the problem of poor projection effects in complex environments caused by traditional manual processing and existing technologies, and improve the projection quality of the projector in complex scenes.

This invention provides an information processing method that can generate an image from an image containing multiple people, with the main person in the composition being the central figure, according to the type of event depicted in the image. [Solution] An information processing method according to one embodiment of the present disclosure acquires an overall image in which multiple people are pictured, identifies the event type of the overall image, identifies a target person from the multiple people according to the identified event type, determines the area containing the target person as a cropping area, and generates individual images by cutting out the cropping area.