Optimized YOLOv5 with Unity 3D for Efficient Gesture Recognition in Complex Machining Environments

Abstract

To improve the efficiency of human-machine interaction in complex machining environments and optimize the accuracy of gesture recognition, a new gesture recognition system is developed by combining the improved You Only Look Once 5 and Unity 3D software. Firstly, an efficient channel attention mechanism is introduced to optimize the network structure of the fifth version of the algorithm to process higher dimensional gesture image data. Secondly, a twin model of complex processing equipment is constructed, and real-time visualization of gesture data and human-machine interaction are achieved using Unity 3D. The research results indicated that the designed static gesture recognition algorithm achieved image signal-to-noise ratio and image intersection to union ratio of 0.95 and 0.98 during the training process. In practical applications, the gesture interaction recognition model designed using this algorithm exhibited extremely low response time, with a minimum of 0.02s to complete the recognition task. At the same time, the recognition accuracy of this model reached up to 99.1%, which was much higher than the other three comparative models. In the practical performance tests, for the different four datasets, the recognition accuracy of YOLOv5-ECA model was 98.5%, 98.7%, 99.1% and 98.8%, with the recognition time as low as 0.07s, 0.02s, 0.11s and 0.08s, respectively. It can be seen that the gesture recognition system provides a new technical solution for human-machine interaction of complex processing equipment, which can further improve the operational efficiency and safety of human-machine interaction.