
Ð’ рамках выпуÑкной квалификационной работы была разработана ÑиÑтема клаÑÑификации и Ñегментации изображений облаков по фотографиÑм Ñо Ñпутниковых Ñнимков. КлаÑÑÐ¸Ñ„Ð¸ÐºÐ°Ñ†Ð¸Ñ Ð¿Ñ€Ð¾Ð²Ð¾Ð´Ð¸Ð»Ð°ÑÑŒ по четырем клаÑÑам: рыба, цветок, гравий, Ñахар. Был проведен анализ методов клаÑÑификации изображений и Ñделан выбор в пользу нейроÑетевого подхода Ñ Ð¸Ñпользованием архитектур vgg16 и mask rcnn.
Graduation qualification work consists of development of a system that is able to classify and segment cloud images from satellite. The classification was done in four classes: fish, flower, gravel, sugar. Also, an analysis of the classification methods was made, and a choice was made in favor of the neural network approach using architectural vgg16 and mask rcnn.
клаÑÑиÑикаÑиÑ, classification, ÑегменÑаÑиÑ, компÑÑÑеÑное зÑение, segmentation, mask rcnn, облака, convolutional neural network, ÑвеÑÑоÑнÑе нейÑоннÑе ÑеÑи, clouds, computer vision, vgg16
клаÑÑиÑикаÑиÑ, classification, ÑегменÑаÑиÑ, компÑÑÑеÑное зÑение, segmentation, mask rcnn, облака, convolutional neural network, ÑвеÑÑоÑнÑе нейÑоннÑе ÑеÑи, clouds, computer vision, vgg16
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