Optimized YOLO-Based Model for Real-Time Hand Keypoint Detection in Robotics

Human hand detection is crucial for robots as they learn human gestures for grasping tasks. However, due to the limited computational power of embedded devices used by robots, many existing approaches fail to meet real-time requirements. To address this challenge, we present an real-time model based on the YOLO framework for human hand keypoint detection. Our approach involves extracting relevant hand data from the COCO dataset to create a specialized dataset tailored for our task. We then design a neural network backbone and decoder grounded in the YOLO architecture, optimizing it for efficient and accurate hand keypoint detection. Our network not only meets real-time performance standards but also demonstrates scalability, allowing for the integration of additional tasks without significant reconfiguration. Through extensive experiments and comparative analysis, we validate the superiority of our model over existing methods. We conclude by discussing the implications of our findings and potential directions for future research.