Сверточная нейронная сеть для классификации степени открытия хлопковой коробочки
Ключевые слова:
computer vision, convolutional neural network, image classification, image segmentation, recurrent neural networks, model training, epoch, layersАннотация
Аннотация — Статья посвящена разработке алгоритма классификации степени раскрытия коробочек хлопка на основе сверточной нейронной сети. В исследовании использовалась нейронная сеть, состоящая из сверточных слоев, слоев субдискретизации и полносвязных слоев. Цель работы – классифицировать образцы коробочек хлопчатника по степени раскрытия. Критериями классификации являются минимизация количества ошибок и достижение высокой точности. В процессе создания алгоритма использовались данные, полученные с помощью компьютерных программ и программ обработки изображений. В данной работе был проведен ряд экспериментов с различными параметрами сверточных нейронных сетей и обучающими выборками для оптимизации процесса классификации. Окончательный алгоритм был протестирован на реальных образцах хлопка и продемонстрировал высокую точность классификации.
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Copyright (c) 2023 Azizjon Abdulhamidov, Erkin Uljaev, Utkirjon Ubaydullayev
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