The amount of data produced every day on the internet increases every day and with the increasing popularity of the social networks the number of published photos are huge, and those pictures contain several implicit or explicit brand logos. Detecting this logos in natural images can provide information about how widespread is a brand, discover unwanted copyright distribution, analyze marketing campaigns, etc. In this paper, we propose a real-time brand logo recognition system that outperforms all other state-of-the-art in two different datasets. Our approach is based on the Single Shot MultiBox Detector (SSD), we explore this tool in a different domain and also experiment the impact of training with pretrained weights and the impact of warp transformations in the input images. We conducted our experiments in two datasets, the FlickrLogos-32 (FL32) and the Logos-32Plus (L32plus), which is an extension of the training set of the FL32. On the FL32, we outperform the state-of-the-art by 2.5% the F-score and by 7.4% the recall. For the L32plus, we surpass the state-of-the-art by 1.2% the F-score and by 3.8% the recall.