Development of Vision Based Sorting System Using Machine Learning for Automated Material Classification

Abstract

The Development of a Vision-Based Sorting System Using Machine Learning for Automated Material Classification aims to design and implement an intelligent sorting mechanism that automates the identification and segregation of materials based on visual features. The proposed system integrates a camera-based vision module, machine learning algorithms, and a robotic sorting mechanism to classify materials efficiently. Using a deep learning model trained on a custom dataset, the system can Detect and categorize objects in real time. The detected information are transmitted to an Arduino-based control unit, which operates a conveyor belt and robotic arm to place each item in its corresponding bin. This prototype demonstrates the potential of computer vision and automation in reducing manual labor and increasing accuracy in industrial sorting applications.