Transfer learning application in a computer vision system for detection of driver distraction

The number of traffic accidents increases every year, and most of these accidents are caused by driver distraction. In countries with less developed road infrastructure, the number of accidents is considerably higher, just like in Brazil. Since distraction is one of the leading causes of accidents, there is a need for mechanisms that prevent drivers from becoming distracted. This paper shows the development of an intelligent image-based driver distraction detection system. Assuming interesting approaches considering neural networks (ANN) to solve the problem based on databases such as State Farm Distracted Driver Detection (SFD3) or AUC Distracted Driver V2 (AUCD2), this study aims to apply the transfer learning technique to obtain better performance and accuracy considering a smaller database. Assuming that the model must have a reduced architecture to be used in an embedded system, models based on convolutional neural networks (CNN) were chosen. Using transfer learning, it was possible to obtain a hit rate of 92.20% in AUCD2 and 64.47% considering the dataset proposed in this study.