Research on Intelligent Waste Sorting System Based on Deep Learning

An efficient waste classification scheme is a crucial measure to promote green development and ecological civilization. This paper designs an intelligent waste sorting system based on deep learning algorithms. The system uses a camera to capture real-time waste images and applies the YOLOv5 algorithm for waste recognition. It trains the model on a dataset of over 3,000 samples. Waste is classified into recyclable waste, kitchen waste, hazardous waste, and other waste. An Arduino microcontroller generates PWM signals to drive a servo gimbal, guiding the waste to the corresponding bin. The experimental results indicate that the system completes sorting within 2 seconds from waste input, achieving an accuracy rate above 95%, and providing an efficient technical solution for intelligent waste sorting.