Otimizando CNNs Com Aprendizado Acumulativo Via Múltiplas Redes Neurais.

Abstract

The number of fatalities in traffic accidents is staggering. Many of these accidents result from disrespect for signaling, which often happens involuntarily, due to distractions, for example. This issue has been treated with great attention in the scientific community. This led to the emergence of Advanced Driver Assistance Systems (ADAS), which are systems that can interpret signaling and flow on the road and, based on this information, issue alerts to the driver or even intervene in driving. Convolutional networks are already widely used in ADAS and are promoting real progress in this area. Thus, this work presents a strategy that uses neural networks in this type of problem. The developed research made a union of the techniques of multiple self-coordinated neural networks and convolutional neural networks, which demonstrated its efficiency when applied to already trained networks. The proposed technique achieved 95.33% accuracy, the possibility of reducing training time and a new strategy to escape local minimums, which opens up a range of new research that can be carried out.