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LOGO-Net: Large-scale Deep Logo Detection and Brand Recognition with Deep Region-based Convolutional Networks

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arxiv 1511.02462 v2 pith:4746FH3L submitted 2015-11-08 cs.CV

Steven C.H. Hoi , Xiongwei Wu , Hantang Liu , Yue Wu , Huiqiong Wang , Hui Xue , Qiang Wu This is my paper · ORCID
classification cs.CV
keywords logodetectiondeeplogo-netrecognitionbrandlarge-scaletechniques
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Logo detection from images has many applications, particularly for brand recognition and intellectual property protection. Most existing studies for logo recognition and detection are based on small-scale datasets which are not comprehensive enough when exploring emerging deep learning techniques. In this paper, we introduce "LOGO-Net", a large-scale logo image database for logo detection and brand recognition from real-world product images. To facilitate research, LOGO-Net has two datasets: (i)"logos-18" consists of 18 logo classes, 10 brands, and 16,043 logo objects, and (ii) "logos-160" consists of 160 logo classes, 100 brands, and 130,608 logo objects. We describe the ideas and challenges for constructing such a large-scale database. Another key contribution of this work is to apply emerging deep learning techniques for logo detection and brand recognition tasks, and conduct extensive experiments by exploring several state-of-the-art deep region-based convolutional networks techniques for object detection tasks. The LOGO-net will be released at http://logo-net.org/

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. LU-500: A Logo Benchmark for Concept Unlearning

    cs.CV 2026-07 conditional novelty 6.0 of 10

    A new 500-company benchmark shows current concept-erasure methods cannot remove small logos from generated images without also changing unrelated content.

  2. Recent Advances in Deep Learning for Object Detection

    cs.CV 2019-08 conditional

    A structured survey of deep learning object detection covering two-stage and one-stage detectors, feature learning, training strategies, applications, and benchmarks up to 2019.

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