Multiple coin detection is a Computer Vision problem which aims at finding coins in an image and classifying their respective values. For Brazilian Real, literature typically consider feature extraction techniques followed by Machine Learning classifiers, specially Artificial Neural Networks. However, some recent results on coin detection with Deep Learning motivated us to propose a hybrid approach for the previously mentioned problem considering localization with traditional Computer Vision techniques and classification with Convolutional Neural Networks. Multiple architectures were tested in a realistic dataset and the proposed solution has mean average precision higher than 92% with classifier accuracy greater than 97%, surpassing other work for the same dataset by 6%.