# Detection of Helmet Use in Motorcycle Drivers Using Convolutional Neural Network
## 5 Conclusions  
In conclusion, it can be said that the research and experimentation work carried out in this article provides a first approach to the generation of new preventive safety systems that guarantee the use of the helmet from the moment the motorcyclist begins his journey until the end of it, based on the fact that such preventive systems become part of the motorcycle and thus really take advantage of the real-time detection of the helmet.In addition, this new motorcycle safety approach of monitoring helmet use from a position on the motorcycle has not been studied based on the literature review conducted in this research. It has been shown that intelligent models that are generated with deep learning algorithms such as CNNs are capable of identifying in real time the absence of essential elements for your safety, such as a helmet, with an accuracy of 97.24%, which means that it presents highly satisfactory results in relation to some of the related works. However, the previously proposed models suggest their implementation in fixed areas, which implies different forms of violation of the surveillance systems of responsible helmet use for the safety of motorcyclists, in addition to the fact that such proposals have the disadvantage that in order to comply with the aim of increase helmet use, they must work in conjunction with other systems, for example databases of registered motorcycles, and not directly with the motorcycle or the driver. Besides, it can be concluded that the amount of data and the \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & **Model Used** & **Video Resolution** & **Accuracy** & **Sensitivity or Recall** & **Specificity** & **AUC** & **Precision** \\ \hline Our proposed work & Retrained Inception V3 & 1920*1080 & 0.9724 & 0.9492 & 0.9964 & 0.9728 & 0.9963 \\ * [PERSON] et al. [17] & Custom CNN & N/M & 0.991 & N/M & N/M & N/M & N/M \\ [PERSON] et al. [19] & Retrained Inception V3 & 1920*1088 & 0.74 & N/M & N/M & N/M & N/M \\ [PERSON] et al. [20] & Custom CNN & 1250*720 & 0.9962 & 1 & N/M & 1 & 1 \\ [PERSON] et al. [21] & Retrained Inception V3 & 1920*1080 & 0.806 & N/M & N/M & N/M & N/M \\ [PERSON] et al. [22] & SAS-YOLOv3-tiny & N/M & 0.782 & 0.809 & N/M & N/M & 0.716 \\ [PERSON] et al. [23] & YOLOv5-HD & 1920*1080 & N/M & 0.972 & N/M & N/M & 0.98 \\ [PERSON] et al. [24] & Faster R-CNN & N/M & 0.9769 & 0.9825 & 0.9694 & N/M & 0.9770 \\ \hline \hline \end{tabular} N/M: the research does not mention the result obtained. *: Results obtained only in the training stage of the model.  
\end{table} 
Table 5: Comparison of results between related works and our proposed work by classification of helmeted and unhelmeted motorcyclists. tools necessary for its processing must be directly related to obtain good results, in this first approximation of our work where data collection has been limited was a good choice decided to use the transfer learning for improve the performance of the model. Although this proposal seeks to improve the safety of motorcyclists and reduce the rate of road accidents caused by these means of transport, there is still much work to be done, which is why the following points are proposed as future work. Reinforce the set of training images with different elements that may confuse the model, increasing the number of test subjects in the experimental phase, thus strengthening the helmet detection model in different situations such as the environment or elements within the area of interest that could hinder the classification process. On the other hand, it should be noted that this work is established as a first approximation for real-time helmet detection in motorcyclists, since specialists in the field of safety and development of intelligent models based on deep learning suggest a larger number of study subjects that can help to improve the classification models. Another work proposal, is to compare different CNN architectures in the classification of helmet use, for example the Wearnet proposed by [PERSON] et al.