Real time detection of traffic signs on mobile device
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In this work we propose a new approach to the object detection problem using Deep Neural Network, in the context of traffic sign detection. Our approach simplifies the detection head complexity by making the requirement for localization lower and taking advantage of our particular task to make the feature extraction model smaller. This strategy allows to create a model running at 88 frames per second on a four years old smartphone, a Samsung S6 (SM-G920T), while maintaining a mAP@50 at 55% and mAP@25 at 68%. To get these results, we created a way to generate data for training based on random geometrical shapes that allows to initialize the weights of our model before training on real data. To the best of our knowledge this model provides the best accuracy over speed ratio for the detection of traffic signs on mobile device at the moment.