The world is heading more and more towards automation, that goes for transportation as well. Various car manufactures already have released level 2 autonomous vehicles meaning that the future is not that far away. An essential part of driving is of course detecting and obeying the traffic signs. Advanced Driver Assistance Systems (ADAS) like speed limit detection systems or a more advanced variant the Traffic Sign Detection (TSD) systems are integrated in top-of-the-line car models in order to increase safety and relieve some ofthe drivers workload. These systems attempt to make drivers aware of incoming signs on the road and warn them against possible danger or in some cases even take control over the vehicle. As in many computer vision systems differences in lighting, weather conditions, motion blur, and poor image quality can reduce the effectiveness of these systems. In case of the Traffic Sign Detection systems other challenges arise like differences in sign position and in the condition (dirty, deformed, discolored etc.) of the sign itself. In order to fully rely on such system all these challenges have to be overcome. The goal of this thesis is to enhance a traffic sign detection model that requires relatively low processing power in such a manner that it can compete with traffic sign models that show great results and achieve high accuracy but require high processing power maintaining the ability to operate in real time. The suggested BoBT method increases the accuracy of a model at the cost of inference speed. There is a small difference in precisely localizing the traffic signs compared to state of the art models but in terms of detecting traffic signs the difference is less than a percent with the highest accuracy model. Not to mention that it still outperforms these models in terms of speed.