A Machine Arm to Assist in Trash Sorting using machine Learning and Object Detection

Abstract

Addressing the global challenge of inefficient waste management, my paper introduces an innovative recycling solution integrating machine learning, computer vision, and a robotic arm [1]. The background problem revolves around inaccurate waste sorting and the environmental impact of recyclables ending up in landfills. The proposed solution involves a sophisticated machine learning model for object recognition, a computer vision system for real-time detection, and a robotic arm for precise object manipulation [2]. Challenges included optimizing the machine learning model for diverse materials and enhancing the robotic arm's adaptability. Experimentation involved testing the system's efficiency in various scenarios, showcasing its ability to recognize and sort recyclables accurately. The results demonstrated promising accuracy and adaptability. Ultimately, this solution offers a practical and automated approach to waste sorting, reducing environmental impact, and promoting efficient recycling practices, making it a valuable tool for waste management systems globally [3].