Development of an Intelligent Surface System for Automated Waste Type Detection and Sorting into Appropriate Containers

Abstract

This research focuses on the development of an intelligent surface system designed to automatically detect and classify different types of waste materials, facilitating sorting into designated garbage containers. The system integrates multiple sensing technologies, including RGB cameras for visual recognition, near-infrared (NIR) spectroscopy for material composition analysis, and capacitive sensors for texture and conductivity data acquisition. Waste item data collected through these sensors is processed using a hybrid machine learning framework, primarily leveraging a convolutional neural network (CNN) for imagebased classification combined with sensor fusion techniques to improve accuracy. The proposed model is trained and validated on a comprehensive dataset comprising diverse waste categories, including plastics (PET, HDPE), metals (aluminum, steel), organic waste, paper, and glass. The classification pipeline achieves an accuracy of over 90%, ensuring reliable real-time sorting. The intelligent surface is coupled with an automated mechanical sorting mechanism that directs detected waste into the appropriate bins, thus preventing contamination and enhancing recycling efficiency. Experimental evaluation includes performance metrics such as classification accuracy, response time, and durability under varying environmental conditions. Results indicate that the system can operate efficiently in real-world scenarios with rapid processing times suitable for public or industrial deployment. This work demonstrates a significant advancement in smart waste management technology by combining sensor fusion, advanced machine learning, and automation, contributing towards sustainable environmental solutions and reducing landfill dependency.