Augmented Reality Maintenance Assistant Using YOLOv5

Maintenance professionals and other technical staff regularly need to learn to identify new parts in car engines and other equipment. The present work proposes a model of a task assistant based on a deep learning neural network. A YOLOv5 network is used for recognizing some of the constituent parts of an automobile. A dataset of car engine images was created and eight car parts were marked in the images. Then, the neural network was trained to detect each part. The results show that YOLOv5s is able to successfully detect the parts in real time video streams, with high accuracy, thus being useful as an aid to train professionals learning to deal with new equipment using augmented reality. The architecture of an object recognition system using augmented reality glasses is also designed.

Automated summary

This study introduces a task assistant model based on a deep learning neural network that utilizes YOLOv5 for car part recognition. A dataset of car engine images was created and the neural network was trained to detect parts in real time, showing high accuracy in detection. Additionally, an object recognition system using augmented reality glasses was also designed.