# Augmented Reality Maintenance Assistant Using YOLOv5
## 6 Object Detection with Deep Learning  
### YOLO Deep Neural Network  
YOLO (You Only Look Once) is one of the most popular deep convolution neural models for object detection, due to its good performance and short time requirements.  
The first model was proposed in 2016, and it is known as YOLOv1. Several updates were made since then, and the latest model is now YOLOv5, released by [PERSON] in 2020. To the best of the authors' knowledge, there is no peer-reviewed research paper  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Mobile Device** & **Videos** & **Total Images** & **Total Labels** & **Targets** \\ \hline Samsung Galaxy S10 & 3 & 582 & 8 & 510 \\ IPhone 11 & 3 & 318 & 8 & 335 \\ Total & 6 & 900 & 8 & 845 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Characteristics of the Datasets created for training and testing the object detector.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**Labels in English** & **Labels in Portuguese** \\ \hline Battery & Bacteria \\ Air filter & Filtro de ar \\ Power steering reservoir & Reservatorio da direcao assistida \\ Engine oil reservoir & Reservatorio de oleo do motor \\ Coolant reservoir & Reservatorio do liquido de arrefecimento \\ Brakes fluid reservoir & Reservatorio do liquido dos travões \\ Wiper water tank & Reservatorio de agua dos limpa-vidros \\ Oil dipstick & Vareta de oleo \\ \hline \hline \end{tabular}
\end{table}
Table 3: Target classes labels translation.  
proposing the YOLOv5 architecture. Nonetheless, there are already more than 240 research papers referring to the architecture. YOLOv5 is based on the PyTorch framework. It is the latest version of the YOLO object recognition model, developed with the continuous efforts of 58 open source contributors [21]. There are a few model configuration files and different versions of the object detector. The present implementation uses YOLOv5s, which is the smallest model, and YOLOv5m, which is the next model in size. The other models available are YOLOv5l and YOLOv5x, the latter being the largest of all. As the network size increases, its performance may also increase, at the cost of additional processing times [22]. Therefore, the larger models may only be useful for complex problems where large datasets are available.  
There are many other deep neural networks that can be used to detect objects. One of them is the mask-RCNN, which aims to solve the instance segmentation problem in machine learning or computer vision. The mask-RCNN is therefore, in theory, more precise, at the cost of additional processing time. In a comparison study, for the task of detecting a sports ball, both YOLO and Mask R-CNN with pre-trained weights show good precision and recall [23].  
Because it is a good and faster detector, with high levels of performance [24], it was decided to choose the YOLOv5 network for the present project. Other architectures, such as the mask-RCNN, could provide similar detection performance and more precise location of the objects. However, the YOLO speed is a great advantage for real time operation of the task assistant. Moreover, in the present case, a bounding box is enough to give good directions to the technician.