ГЛУБОКИЕ НЕЙРОННЫЕ СЕТИ КАК ИНСТРУМЕНТ ПОВЫШЕНИЯ ЭФФЕКТИВНОСТИ СОРТИРОВКИ ПЛАСТИКОВЫХ ОТХОДОВ

В индустрии вторсырья наблюдается острая потребность в качественном сортированном материале. Проблематика сортировочных центров, связанная с трудностями сортировки и очистки пластика, приводит к накоплению отходов на свалках вместо их переработки, подчеркивая необходимость развития эффективных автоматизированных методов сортировки. В этом исследовании предлагается интеллектуальная модель классификации пластиков, разработанная на основе сверточной нейронной сети (CNN) с использованием таких архитектур, как MobileNet, ResNet и EfficientNet. Модели были обучены на наборе данных, состоящем из более чем 4000 изображений, распределенных по пяти категориям пластика. Среди протестированных архитектур EfficientNet-SED продемонстрировала самую высокую точность классификации – 99,1%, что соответствует результатам предыдущих исследований в этой области. Эти результаты подчеркивают потенциал использования передовых архитектур CNN для повышения эффективности процессов переработки пластика.

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