## 3 Results
### First Step Validation  
Figure 2 shows a brief scheme of the validation configuration. As can be seen, we performed two different training processes, one using the KITTI labels and, in order to compare our results, a training process using the auto-generated labels by our methodology.  
To check whether the target of generating an auto-labeled dataset has been met, it is necessary to consult for each model in Table 1 the rows about OpenPCDet performance and the rows with our auto-generated labels.  
The performance of each model is measured in the accuracy, which measures the percentage of valid labels generated by each model. Not only is the accuracy of the auto-generated labels comparable with manual human annotators, but the performance shown by models trained from scratch with auto-generated labels is also comparable with the models trained with KITTI. Table 1 shows a slight deviation between the results obtained using manual labels with respect to the auto-generated labels proposed in the present work.  
Looking through Table 1, the results show that there is a slight difference between each model taken from the literature in KITTI and from scratch models that have been trained with auto-generated labels. These differences may be even smaller without doing anything particularly differently, since the weights of each model's networks are extensively investigated for best performance for each model using the KITTI dataset and labels, running as many training processes as needed for this purpose. However, the purpose of this work is not to optimize the results, but to show that the use of the mentioned labels is reliable, so we performed testing using the same configuration as the original models without much work to fine tune the network. Therefore, this Table should be used as a comparison of the performance between using the manually labeled dataset against the auto-generated labels proposed in the current methodology, and not as standalone results.  
A more detailed table analysis shows that the easier the object is to locate, the better the performance of the auto-generated labels. Breaking down the results into easy, moderate and hard, the average difference between the original is around 1.2% on easy, 2.1% on  
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **Easy** & **Moderate** & **Hard** \\ \hline F-Score & 0.957 & 0.927 & 0.740 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Auto-labeling performance of the proposed YOLOv5 model with images of the KITTI raw data set.  
Figure 2: Diagram of the experiment configuration.  
moderate and 3.16% on hard respect BEV metrics. These small differences show the efficiency of the model in obtaining results similar to those of the original model.  
This variability in the results is justified because of the difference between the ground truth bounding boxes present on the KITTI dataset and the auto-generated labels of our methodology. As can be seen in Figure 3, there is a slight variation in the bounding boxes of both methods. The error calculation on the model performance also takes into account the position and dimension of the bounding box, which can lead to reduced performance when comparing the KITTI ground truth against our model results. This penalty on the error is caused by the location and dimension of the bounding box and not by vehicle detection errors, which produced the above-mentioned result variation.