Abstract
The lack of helmet use in motorcyclists is one of the main risk factors with severe consequences in traffic accidents. Wearing a certified motorcycle helmet can reduce the risk of head injuries by 69% and fatalities by 42%. At present there are systems that detect the use of the helmet in a very precise way, however they are not robust enough to guarantee a safe journey, that is why is proposed an intelligent model for detecting the helmet in real time using training images of a camera mounted on the motorcycle, and convolutional neural networks that allow constant monitoring of the region of interest to identify the use of the helmet. As a result, a model was obtained capable of identifying when the helmet is used or not in an objective and constant manner while the user is making a journey, with a performance of 97.24%. Thus, it was possible to conclude that this new safety perspective provides a first approach to the generation of new preventive systems that help reduce accident rates in these means of transport. As future work, it is proposed to improve the model with different images that may violate the helmet detection.
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