Robust Logo Detection in E-Commerce Images by Data Augmentation

Logo detection is an important task in the intellectual property protection in e-commerce. In the paper, we introduce our solution for the ACM MM2021 Robust Logo Detection Grand Challenge. The competition requires the detection of logos (515 categories) in e-commerce images. This competition is challenged by long-tail distribution, small objects, and different types of noises. To overcome these challenges, we built a highly optimized and robust detector. We first tested many effective techniques for general object detection and then focused on data augmentation. We found that data augmentation was effective in improving the performance and robustness of logo detection. Based on the combination of these techniques, we achieved APs of 64.6% and 61.3% on the clean and noisy datasets respectively, which were improved by 8.1% and 19.5% relative to the official baseline. We ranked 5th among 36489 teams in the competition.