In this paper we propose a method for logo recognition using deep learning. Our recognition pipeline is composed of a logo region proposal followed by a Convolutional Neural Network (CNN) specifically trained for logo classification, even if they are not precisely localized. Experiments are carried out on the FlickrLogos-32 database, and we evaluate the effect on recognition performance of synthetic versus real data augmentation, and image pre-processing. Moreover, we systematically investigate the benefits of different training choices such as class-balancing, sample-weighting and explicit modeling the background class (i.e. no-logo regions). Experimental results confirm the feasibility of the proposed method, that outperforms the methods in the state of the art.
Very recently, Bianco et al. REF proposed a deep learning pipeline investigating the benefits of different training choices such as class-balancing, data augmentation, contrast normalization, sample-weighting and explicit modelling of background class.
Deep Learning for Logo Recognition
Neurocomputing 245, 23-30 (2017)
Computer Science