Automatic Graphic Logo Detection via Fast Region-based Convolutional Networks

TL;DR

This paper presents a robust automatic graphic logo detection system using transfer learning with Fast R-CNN, achieving superior results over traditional methods on the FlickrLogos-32 dataset.

Contribution

It introduces the novel application of transfer learning with Fast R-CNN for logo detection, improving accuracy under various imaging conditions.

Findings

High detection accuracy on FlickrLogos-32 dataset

Outperforms state-of-the-art hand-crafted feature systems

Effective under noise and transformations

Abstract

Brand recognition is a very challenging topic with many useful applications in localization recognition, advertisement and marketing. In this paper we present an automatic graphic logo detection system that robustly handles unconstrained imaging conditions. Our approach is based on Fast Region-based Convolutional Networks (FRCN) proposed by Ross Girshick, which have shown state-of-the-art performance in several generic object recognition tasks (PASCAL Visual Object Classes challenges). In particular, we use two CNN models pre-trained with the ILSVRC ImageNet dataset and we look at the selective search of windows `proposals' in the pre-processing stage and data augmentation to enhance the logo recognition rate. The novelty lies in the use of transfer learning to leverage powerful Convolutional Neural Network models trained with large-scale datasets and repurpose them in the context of graphic logo detection. Another benefit of this framework is that it allows for multiple detections of graphic logos using regions that are likely to have an object. Experimental results with the FlickrLogos-32 dataset show not only the promising performance of our developed models with respect to noise and other transformations a graphic logo can be subject to, but also its superiority over state-of-the-art systems with hand-crafted models and features.