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