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arXiv · 1911.07440

Large Scale Open-Set Deep Logo Detection

Muhammet Bastan·Hao-Yu Wu·Tian Cao·Bhargava Kota·Mehmet Tek
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Abstract

We present an open-set logo detection (OSLD) system, which can detect (localize and recognize) any number of unseen logo classes without re-training; it only requires a small set of canonical logo images for each logo class. We achieve this using a two-stage approach: (1) Generic logo detection to detect candidate logo regions in an image. (2) Logo matching for matching the detected logo regions to a set of canonical logo images to recognize them. We constructed an open-set logo detection dataset with 12.1k logo classes and released it for research purposes.We demonstrate the effectiveness of OSLD on our dataset and on the standard Flickr-32 logo dataset, outperforming the state-of-the-art open-set and closed-set logo detection methods by a large margin. OSLD is scalable to millions of logo classes.

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Muhammet Bastan, Hao-Yu Wu, Tian Cao, Bhargava Kota, Mehmet Tek. 2022-03-12. Large Scale Open-Set Deep Logo Detection. https://arxiv.org/abs/1911.07440

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