Implementation of YOLOv9-Based Real-Time Object Detection for Robotic Finger Applications

摘要

The project investigates the integration of YOLO v9, the latest iteration of the “You Only Look Once” model, into robotic fingers to enhance their precision, responsiveness, and versatility in object handling. YOLO v9's superior object detection capabilities—especially its real-time processing speed and high accuracy—make it an ideal candidate for advancing robotic manipulation. By training YOLO v9 on an extensive dataset encompassing diverse object shapes, sizes, and textures, this project enables robotic fingers to recognize and respond to various physical characteristics with greater nuance. Through this approach, robotic systems gain an adaptive edge, adjusting their grip and interaction based on specific object features, thus expanding their utility in precision-demanding fields such as manufacturing, healthcare, and logistics. The research not only explores technical integration but also assesses the impact of this enhanced object recognition on robotics' overall reliability and efficiency in handling real-world tasks. The findings could contribute to the development of more intelligent, responsive, and adaptable robotic systems, redefining automation standards in critical industries.