A Scalable Data Augmentation and Training Pipeline for Logo Detection

Abstract

Logo detection in images is particularly challenging due to limited access to well-labelled data. Many existing logo detection methods are not scalable to larger datasets due to tedious bounding-box annotation work. As a result, with only a small number of logo classes and limited well-labelled images per class, their performance deteriorates on real-world applications. In this work, we propose a data augmentation and training pipeline to tackle these challenges. Specifically, we develop an incremental learning approach that starts training using synthetic data, followed by iteratively obtaining real training images from a given source and updating the current model with the newlyobtained data. To avoid model drift, we add a human curation step where incorrect detections (false-positives) are filtered out by simple-clicks using a User Interface, we designed. With this approach, we were able to generate a large (173,000 images of 173 logo classes) dataset termed Logo173 where all images are annotated with bounding-boxes. This image dataset can also be used to train a frame-by-frame baseline logo detector for videos. We demonstrate with extensive experiments that the proposed pipeline significantly saves time and effort for tedious data annotation and outperforms a current state-of-the-art logo detection method.