Analysis of object recognition systems using augmented reality glasses

Object recognition systems and augmented reality devices aim to bridge the gap between the virtual and real worlds. It is natural, then, to combine these two technologies to create devices that can assist us in various aspects of life. This paper compares four object detection models: Faster R-CNN ResNet-101 v1, YOLO v8s, SSD MobileNet v2, and EfficientDet Lite 2 in the context of their use with augmented reality glasses. Using a simulated test environment, we examined resource consumption, energy efficiency, precision, and speed of the models during real-time operation. The results confirm that models using single-stage architecture are more suitable for real-time operation directly on the device. Among the single-stage models, YOLO v8s proved to be the most efficient.