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.

本文介绍了一种利用快速区域卷积神经网络模型的自动图形标志检测系统，通过使用预处理阶段的候选窗口选择性搜索和数据增强提高标志识别率，实现了对图形标志的多重检测。实验结果表明，与手工模型以及其他特征的最新系统相比，该模型在噪声和其他转换下具有出色的性能。

基于快速区域卷积网络的自动图形Logo检测