Color Sorting System Using YOLOv5 for Robotic Mobile Applications

Abstract

The integration of mobile robots and computer vision has revolutionized industrial tasks by enabling precise and efficient automation processes. This work proposes a mobile platform for sorting Petri dishes using advanced deep-learning techniques. We utilize the YOLOv5 framework for real-time color detection and a 6-bar mechanism with a gripper for dynamic sample sorting. The implementation enhances logistics and reduces operational errors through accurate color classification. Our methodology includes creating a training dataset of over one thousand labeled RGB images and validating the system's performance. The trained network achieved over 90% accuracy during validation and testing, demonstrating precise robot positioning and effective Petri dish manipulation. This research successfully addresses automation challenges in industrial settings, offering improved efficiency and accuracy.