Recent Advances in LOGO Detection Using Machine Learning Paradigms: Theory and Practice

This book presents the current trends in deep learning-based object detection framework with a focus on logo detection tasks. It introduces a variety of approaches, including attention mechanisms and domain adaptation for logo detection, and describes recent advancement in object detection frameworks using deep learning. We offer solutions to the major problems such as the lack of training data and the domain-shift issues.

This book provides numerous ways that deep learners can use for logo recognition, including:

• Deep learning-based end-to-end trainable architecture for logo detection
• Weakly supervised logo recognition approach using attention mechanisms
• Anchor-free logo detection framework combining attention mechanisms to precisely locate logos in the real-world images
• Unsupervised logo detection that takes into account domain-shift issues from synthetic to real-world images
• Approach for logo detection modeling domain adaption task in the context of weakly supervised learning to overcome the lack of object-level annotation problem.

The merit of our logo recognition technique is demonstrated using experiments, performance evaluation, and feature distribution analysis utilizing different deep learning frameworks.

The book is directed to professors, researchers, practitioners in the field of engineering, computer science, and related fields as well as anyone interested in using deep learning techniques and applications in logo and various object detection tasks.