Discriminative Semantic Feature Pyramid Network with Guided Anchoring for Logo Detection

Baisong Zhang, Sujuan Hou (), Awudu Karim, Jing Wang, Weikuan Jia and Yuanjie Zheng
Additional contact information
Baisong Zhang: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Sujuan Hou: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Awudu Karim: School of Engineering, Beijing University of Technology, Beijing 101303, China
Jing Wang: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Weikuan Jia: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China
Yuanjie Zheng: School of Information Science and Engineering, Shandong Normal University, Jinan 250358, China

Mathematics, 2023, vol. 11, issue 2, 1-20

Abstract: Logo detection is a technology that identifies logos in images and returns their locations. With logo detection technology, brands can check how often their logos are displayed on social media platforms and elsewhere online and how they appear. It has received a lot of attention for its wide applications across different sectors, such as brand identity protection, product brand management, and logo duration monitoring. Particularly, logo detection technology can offer various benefits for companies to help brands measure their logo coverage, track their brand perception, secure their brand value, increase the effectiveness of their marketing campaigns and build brand awareness more effectively. However, compared with the general object detection, logo detection is more challenging due to the existence of both small logo objects and large aspect ratio logo objects. 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 two components, namely Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). The former is proposed to fuse semantic features into low-level feature maps to obtain discriminative representation of small logo objects, while the latter is further integrated into DSFP to generate large aspect ratio anchor boxes for detecting large aspect ratio logo objects. Extensive experimental results on four benchmarks demonstrate the effectiveness of the proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the strength of the proposed DSFP-GA when detecting both small logo objects and large aspect logo objects.

Keywords: object detection; discriminative semantic features; small logo; large aspect ratio logo; logo detection (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/2227-7390/11/2/481/pdf (application/pdf)
https://www.mdpi.com/2227-7390/11/2/481/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jmathe:v:11:y:2023:i:2:p:481-:d:1037523

Access Statistics for this article

Mathematics is currently edited by Ms. Emma He

More articles in Mathematics from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().