Enhancing Efficiency and Safety with YOLOv5-Powered Robotic Arms for Waste Classification

摘要

The world is experiencing a transformation shift from manual labor to digital solutions, making work simpler and more efficient while enhancing the quality of life globally. A prime example of this shift is the Object Picking Robotic Arm (OPRA). Designed to operate with minimal human intervention, the OPRA reduces the risk of physical injuries among workers by replacing human labor with robotic precision. This technology finds applications in both industrial and domestic settings, including the automotive industry, metalworking, chemical processing, and various pick-and-place tasks. In this research, we develop a robotic system for automated waste picking and sorting. This system utilizes the YOLOv5 object detection algorithm to achieve high accuracy (95\%) and precision (90\%) in classifying five common waste categories: cardboard, metal, paper, plastic, and trash.