Deep Learning for Logo Detection: A Survey

TL;DR

This survey reviews recent deep learning methods for logo detection, covering datasets, strategies, applications, challenges, and future directions in this rapidly evolving field.

Contribution

It provides a comprehensive overview of deep learning-based logo detection techniques, datasets, and applications, highlighting current challenges and future research directions.

Findings

Diverse and challenging datasets facilitate performance evaluation.

Deep learning strategies vary in strengths and weaknesses.

Logo detection applications span transportation, brand monitoring, and copyright enforcement.

Abstract

When logos are increasingly created, logo detection has gradually become a research hotspot across many domains and tasks. Recent advances in this area are dominated by deep learning-based solutions, where many datasets, learning strategies, network architectures, etc. have been employed. This paper reviews the advance in applying deep learning techniques to logo detection. Firstly, we discuss a comprehensive account of public datasets designed to facilitate performance evaluation of logo detection algorithms, which tend to be more diverse, more challenging, and more reflective of real life. Next, we perform an in-depth analysis of the existing logo detection strategies and the strengths and weaknesses of each learning strategy. Subsequently, we summarize the applications of logo detection in various fields, from intelligent transportation and brand monitoring to copyright and trademark compliance. Finally, we analyze the potential challenges and present the future directions for the development of logo detection to complete this survey.