10 results about "GrabCut" patented technology

The application discloses a kind of extremely high resolution original drawing image layering segmentation method, equipment, medium and product, it is related to image segmentation field, the method includes obtaining extremely high resolution to be segmented original drawing image;Using SAM model to the original drawing image to be segmented is preliminarily segmented, and obtains preliminary segmentation result;Multiple initial mask regions are contained in preliminary segmentation result;Using GrabCut algorithm, the preliminary segmentation result is globally optimized, edge optimization and residual hole completion, and obtains the segmentation result after optimization;Using Poisson equation, the edge of the segmentation result after optimization is reconstructed, and obtains final segmentation result.The application can be segmented according to the needs of oneself to different granularity size.In the segmentation process, the application realizes maximum degree of automation using SAM model and GrabCut algorithm, in addition to this, the application optimizes the edge of last segmentation result, greatly improves the efficiency and accuracy of image segmentation.

The invention discloses a photovoltaic panel surface defect detection method based on improved YOLOv5s, and the method comprises the steps: collecting a surface image of a photovoltaic panel through a visible light camera carried by an unmanned plane, and segmenting a defect region through a GrabCut algorithm; according to the method, adaptive anchor frame calculation is performed on an input image, a Mosaic data enhancement technology is applied, a diversity training sample is generated, and a lightweight network Ghost Net is introduced to replace a backbone network of original YOLOv5s so as to reduce model complexity, a CIoU loss function of the original YOLOv5s is replaced by a SIoU loss function, a model convergence speed is accelerated, detection and positioning precision is improved, and a classification task and a regression task are separated. The average precision, the accuracy rate, the recall rate, the parameter quantity and the like are used as evaluation indexes, an ablation experiment and a contrast experiment are performed on the data set, and high-precision real-time effective detection on the surface defect image of the aerial photovoltaic panel is realized.

The invention provides a hardware corrosion detection and quantification method in an electric power inspection image, and relates to the technical field of electric power inspection image processing, and the method comprises the following steps: collecting an unmanned aerial vehicle inspection power transmission line aerial image; positioning hardware equipment in the image by using the improved YOLOv5s; separating the hardware fitting from the background by using a GrabCut segmentation algorithm and a morphological optimization algorithm; extracting and separating color images of three channels in a YCrCb color space, and obtaining a grayscale image of a Cr channel; according to the threshold segmentation of the Cr channel, obtaining a hardware corrosion binary image, and according to the binary image, judging whether the hardware is corroded or not; and counting the number of pixel points in the corrosion area and the number of pixel points in the hardware fitting area, and judging the corrosion grade of the hardware fitting by calculating the area ratio of the two areas. According to the method, deep learning and traditional image processing are combined, hardware identification, corrosion area detection and corrosion degree quantification in the inspection image are realized, and the defects of low working efficiency and high subjectivity caused by manual visual inspection of corrosion detection of power transmission line hardware equipment are overcome.

The present application belongs to the technical field of image segmentation, and particularly relates to a real-time image segmentation method for directional solidification dendrite growth morphology, which comprises the following steps: in a real-time image, calculating the periodic response intensity at a pixel point according to the modulus operation result of the distance from the pixel point to the nearest grain along the vertical direction of the local dominant direction vector of the pixel point and the equivalent dendrite spacing in the image space, determining the periodic term according to the periodic response intensity at all pixel points, reconstructing the energy function of the GrabCut algorithm based on the periodic term, minimizing the reconstructed energy function to realize the optimal segmentation of the real-time image, and obtaining the segmentation result of the real-time image. The present application significantly improves the resolution capability for the densely arranged and texture consistent dendrite structure, and solves the fundamental problem of "grain boundary adhesion".

The invention discloses a remote sensing image farmland area determination method based on improved GrabCut and semantic segmentation. The method comprises the steps of obtaining a satellite remote sensing image of a target area, sequentially performing radiometric calibration, atmospheric correction and geometric correction on the satellite remote sensing image, extracting a farmland area based on an improved GrabCut algorithm, and obtaining a satellite farmland segmentation mask image. Images of agricultural areas in multiple scenes are collected through the unmanned aerial vehicle, image enhancement is carried out, and a multi-scene farmland image data set is constructed. And training the improved U-Net + + model by using the multi-scene farmland training image set to obtain an unmanned aerial vehicle farmland segmentation mask image. Meanwhile, based on a collaborative application mechanism of a satellite and an unmanned aerial vehicle, selection of two remote sensing image calculation methods is realized. Finally, the area of the farmland in each remote sensing image is obtained through the obtained spatial resolution of the remote sensing images, the precision of the result obtained through the method is higher than that of a traditional calculation method, the calculation efficiency is improved, and higher universality is achieved.

The invention belongs to the technical field of image segmentation, and particularly relates to a real-time image segmentation method for a dendritic crystal growth form of a directional solidification furnace, which comprises the following steps of: in a real-time image, according to a modular operation result of a distance from a pixel point to a nearest crystal grain along a vertical direction of a local dominant direction vector of the pixel point and an equivalent dendritic crystal spacing in an image space, calculating the dendritic crystal growth form of the directional solidification furnace; calculating the periodic response intensity at the pixel points, and determining a periodic term according to the periodic response intensity at all the pixel points; reconstructing an energy function of the GrabCut algorithm based on the periodic term; and by minimizing the reconstructed energy function, optimal segmentation of the real-time image is realized, and a segmentation result of the real-time image is obtained. According to the method, the distinguishing capability of densely arranged dendritic crystal structures with consistent textures is remarkably improved, and the fundamental problem of grain boundary adhesion is solved.

The present application relates to a kind of based on deep learning fusion multi-source image's lettuce phenotype parameter estimation method, including the following steps: obtaining RGB image and Depth image, manually measure the phenotype parameter of each lettuce sample, form image dataset;Using GrabCut algorithm carries out lettuce image foreground segmentation, using Z-Score method to the normalization of lettuce image data;Construction and training lettuce phenotype parameter estimation deep learning multi-source data fusion model.The beneficial effects of the present application are: the method of the present application adopts deep learning technology, fuses visible light image and depth image features, utilizes lettuce phenotype parameter estimation deep learning multi-source data fusion model to accurately estimate lettuce phenotype parameter by multi-source image information;As can be seen from the experimental results, the present application can successfully fuse two-dimensional RGB image and Depth image, excellent performance, and has important application value for high-throughput growth monitoring and yield estimation of protected vegetable.

The invention discloses an ancient topographic map place name identification method and system, and relates to the field of computer vision. And a two-stage strategy of character area detection and character recognition is designed, so that the end-to-end accuracy of place name recognition is effectively improved. According to the method, through processing of first image cutting and then detection in a first stage, adverse effects caused by too large scale difference between images and characters are reduced, accurate positioning of character areas is realized, and reasoning time is shortened; a space attention cross-dimension reconstruction module is utilized in a first-stage algorithm, and space, channel and frequency domain features are combined, so that the feature extraction capability of the model is improved, the space redundancy is reduced, and the adaptability of the model to variable fonts, variable directions, variant characters and the like in an ancient map is enhanced; in the second stage, in the preprocessing process, background features are weakened by adopting grey-scale map conversion, filtering and GrabCut algorithms, so that character features are highlighted; the original branch and the newly-added branch based on the image recovery module are used for cooperatively recognizing the characters, and the accuracy of low-quality character image recognition is improved.

This invention discloses a method for food image segmentation and removal based on instance segmentation and spatial prior clustering, comprising three steps: Step 1, inferring from the photo using the YOLO instance segmentation model, extracting the segmentation mask, and cropping it into a black background image; Step 2, extracting the foreground from the image using the GrabCut algorithm, converting it to a transparent background, and generating an RGBA format image; Step 3, performing spatial partitioning on the transparent background image based on Euclidean distance transformation, using the outer perimeter as the plate prior and the center as the food prior, performing K-means clustering in the Lab color space, automatically identifying the plate and food clusters based on the spatial enrichment ratio of each cluster at the perimeter and center, and removing plate pixels based on external reachability connectivity verification, outputting a transparent background image containing only the food. This invention eliminates the need for manual region labeling or setting color thresholds, can adaptively handle plates of different colors, shapes, and sizes, is suitable for diverse dining scenarios, and supports tasks such as food recognition and nutritional estimation.

The application discloses a farmland area measurement method based on improved GrabCut and semantic segmentation of remote sensing images, and comprises the following steps: acquiring satellite remote sensing images of a target area; performing radiation calibration, atmospheric correction and geometric correction on the satellite remote sensing images in sequence; extracting a farmland area based on an improved GrabCut algorithm to obtain a satellite farmland segmentation mask image; collecting images of agricultural areas in multiple scenes through a UAV, and performing image enhancement to further construct a multi-scene farmland image dataset; training an improved U-Net++ model using the multi-scene farmland training image set to obtain a UAV farmland segmentation mask image; simultaneously based on a cooperative application mechanism of satellites and UAVs, selecting two kinds of remote sensing image calculation methods; and finally obtaining the area of farmland in each remote sensing image through the spatial resolution of the obtained remote sensing images. The result obtained by the application has higher accuracy than that of a traditional calculation method, the calculation efficiency is improved, and the application has higher universality.