# Detection of Helmet Use in Motorcycle Drivers Using Convolutional Neural Network
## 1 Introduction
As a result of this, several studies have been developed to try to solve the problem of the lack of helmet use and thus reduce deaths and injuries in road accidents involving motorcycles. Government's in different countries are trying to mitigate this problem by implementing flexible, effective and low-cost methods. Consequently, Intelligent Transportation Systems (ITS) have been developed, which are advanced applications that combine electronics, communication systems, computers and sensors. These ITS integrate vehicles, their users and the roads to provide real-time information to increase safety in traffic systems. In the development of various ITS for motorcycle and helmet detection, different technologies have been used, such as radar, sound sensors, optical sensors, among others [15]. The available technologies for motorcycle detection and recognition also face two major problems: the computational capacity (since most of them are deployed on the road) and the degree of detection reliability [16]. An example of this is the surveillance methods that use video cameras installed at specific points on the roads to identify offenders who do not wear helmets while riding motorcycles; however, these systems are passive and require human assistance, so their efficiency decreases over a prolonged period of time. Automation of these types of systems can help to have reliable and robust monitoring, as well as significant reducing the amount of human resources necessary for its operation [17], but, taking into account that in most of the streets there are no cameras that are monitoring the traffic, installing cameras in all places cannot be a viable and economically sustainable solution [18]. Related Work  
In recent years, combining different image processing techniques with intelligent models has shown to be a good proposal in the task of detecting helmets in motorcyclists,because the machine learning algorithms, specially the deep learning algorithms can efficiently recognize a helmet in an image or video [17]. [PERSON] et al. [16] proposes to analyze traffic videos with an artificial intelligence model, which uses a pre-trained convolutional neural network to classify motorcycles and detect the use of protective helmet from the morphology of the object, this model obtained as results for the detection of motorcycles an accuracy of 97.14% and 85.29% for the detection of the helmet. In the work of [PERSON] et al. [17], they reports the accuracy obtained in the training stage of the models, where the use of machine learning algorithms as classifier varies, which are K-nearest neighbor, decision trees, support vector machines and convolutional neural networks, the latter being the ones that have delivered the best results with 99.1% accuracy in the classification of drivers with and without helmet. Another example of the use of these tools is the one proposed by [PERSON] et al. [19] where from analyzing traffic videos, the helmet detection model in which the InceptionV3 pre-trained convolutional neural network is used, a 74% accuracy was obtained in the validation stage of the proposal. Similarly, [PERSON] et al. [20], propose identify the use of helmet, analyzing traffic videos obtained from a fixed position, with the difference that they use a custom convolutional neural network, reaching an accuracy of 96.98%. [PERSON] et al. [21] proposes a model by detecting helmet use where Inception V3 retraining is used together with the Multiple Transfer Learning Technique (MTL), achieving a final test accuracy of 80.6%. Other proposals use object detection algorithms based on convolutional neural networks belonging to the YOLO (You Only Look Once) series to perform the helmet detection task from images obtained of the urban traffic. [PERSON] et al. [22] uses the algorithm called SAS-YOLOv3-tiny that obtained 71.6% in the accuracy metric and 80.9% for Recall in its testing stage. Similarly [PERSON] et al.