5 results about "GrabCut" patented technology

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 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 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.

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.