Discriminative Semantic Feature Pyramid Network with Guided Anchoring
for Logo Detection
- URL: http://arxiv.org/abs/2108.13775v1
- Date: Tue, 31 Aug 2021 11:59:00 GMT
- Title: Discriminative Semantic Feature Pyramid Network with Guided Anchoring
for Logo Detection
- Authors: Baisong Zhang, Weiqing Min, Jing Wang, Sujuan Hou, Qiang Hou, Yuanjie
Zheng, Shuqiang Jiang
- Abstract summary: We propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA)
Our approach mainly consists of Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA)
- Score: 52.36825190893928
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recently, logo detection has received more and more attention for its wide
applications in the multimedia field, such as intellectual property protection,
product brand management, and logo duration monitoring. Unlike general object
detection, logo detection is a challenging task, especially for small logo
objects and large aspect ratio logo objects in the real-world scenario. In this
paper, we propose a novel approach, named Discriminative Semantic Feature
Pyramid Network with Guided Anchoring (DSFP-GA), which can address these
challenges via aggregating the semantic information and generating different
aspect ratio anchor boxes. More specifically, our approach mainly consists of
Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA).
Considering that low-level feature maps that are used to detect small logo
objects lack semantic information, we propose the DSFP, which can enrich more
discriminative semantic features of low-level feature maps and can achieve
better performance on small logo objects. Furthermore, preset anchor boxes are
less efficient for detecting large aspect ratio logo objects. We therefore
integrate the GA into our method to generate large aspect ratio anchor boxes to
mitigate this issue. Extensive experimental results on four benchmarks
demonstrate the effectiveness of our proposed DSFP-GA. Moreover, we further
conduct visual analysis and ablation studies to illustrate the advantage of our
method in detecting small and large aspect logo objects. The code and models
can be found at https://github.com/Zhangbaisong/DSFP-GA.
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