Recent Advances in Logo Detection Using Machine Learning Paradigms: Theory and Practice: 255 - Rilegato

Chen, Yen-wei; Ruan, Xiang; Jain, Rahul Kumar

Rilegato

ISBN 10: 3031598105 ISBN 13: 9783031598104

Casa editrice: Springer-Nature New York Inc, 2024

Riassunto

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.

 

 

 

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Informazioni sull?autore

Dalla quarta di copertina

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 modelingdomain 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.

 

 

 

Dettagli dell'edizione

• Editore: Springer-Nature New York Inc
• Data di pubblicazione: 2024
• Lingua: Inglese
• ISBN 10: 3031598105
• ISBN 13: 9783031598104
• Rilegatura: Copertina rigida
• Numero di pagine: 132
• Contatto del produttore: non disponibile
• Persona responsabile: Springer Nature Customer Service Center GmbH; ProductSafety@springernature.com; ; Europaplatz 3,69115 Heidelberg, Germany; Heidelberg; 69115; Germania