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

• Record Nr.: UNINA9910865235503321
• Autore: Chen Yen-Wei
• Pubbl/distr/stampa: Cham : , : Springer International Publishing AG, , 2024; ©2024
• ISBN: 3-031-59811-3
• Edizione: [1st ed.]
• Descrizione fisica: 1 online resource (128 pages)
• Collana: Intelligent Systems Reference Library ; ; v.255
• Altri autori (Persone): RuanXiang; JainRahul Kumar
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di contenuto: Intro -- Preface -- Contents -- About the Authors -- 1 Deep Convolutional Neural Networks -- 1.1 Deep Learning Frameworks -- 1.1.1 Core Component and Key Elements of Deep Learning -- 1.2 Feature Extraction Networks -- 1.2.1 VGG: Very Deep Convolutional Networks for Large-Scale Image Recognition -- 1.2.2 Residual Networks -- 1.2.3 Deep Layer Aggregation Networks -- 1.2.4 Hourglass Framework -- 1.3 Object Detection Frameworks: Detection Head -- 1.3.1 Detection Head Functionality in Object Detection Frameworks -- 1.3.2 Anchor Box-Based Detection Frameworks -- 1.3.3 Anchorfree Detection Frameworks -- 1.4 Summary -- References -- 2 Introduction to Logo Detection -- 2.1 Logo Detection and Its Applications -- 2.2 Logo Detection Challenges -- 2.3 Related Work in Logo Detection -- 2.3.1 Deep Learning for Logo Detection -- 2.4 Proposed Approaches for Logo Detection -- 2.5 Summary -- References -- 3 Weakly Supervised Logo Detection Approach -- 3.1 Weakly-Supervised Logo Detection Using Image-Level Annotation -- 3.2 Attention Mechanisms -- 3.3 Weakly Supervised Logo Detection with Dual-Attention Dilated Residual Network -- 3.3.1 Feature Extraction Backbone Network -- 3.3.2 Spatial Attention Mechanism -- 3.3.3 Channel Attention Mechanism -- 3.3.4 Gradient-Based Grad-CAM for Localization of Logos -- 3.3.5 Implementation of Channel and Spatial Attention -- 3.4 Experiments and Results -- 3.4.1 ; Implementation -- 3.4.2 Dataset -- 3.4.3 Evolution Measures -- 3.4.4 Comparison with Different Attention Modules -- 3.5 Summary -- References -- 4 Anchorfree Logo Detection Framework -- 4.1 Dual-Attention LogoNet for Logo Detection -- 4.1.1 Overview of the Logo Detection Framework -- 4.1.2 Layer-Aggregated Hourglass Style Feature Extraction Network -- 4.1.3 Attention Modules -- 4.1.4 Detection Head -- 4.1.5 Overall Framework of Dual-Attention LogoNet.; 4.1.6 Lightweight CNNs Network Architecture for Practical Applications -- 4.1.7 Experiments and Results -- 4.1.8 Implementation -- 4.1.9 Evaluation on FlickrLogos-32 Dataset -- 4.1.10 Evaluation with Lightweight CNNs Method -- 4.2 Summary -- References -- 5 Mitigating Domain Shift in Logo Detection: An Adversarial Learning-Based Approach -- 5.1 Domain Shift Problem -- 5.2 Domain Adaptation for Computer Vision Tasks -- 5.3 Related Work: Domain Adaptation -- 5.4 Adaptation Using Anchorfree Object Detector for Logo Detection -- 5.5 Evaluation with Adversarial-Based Domain Adaptation Using LogoNet -- 5.5.1 Experiments and Results -- 5.6 Summary -- References -- 6 Unsupervised Logo Detection with Adversarial Domain Adaptation from Synthetic to Real Images -- 6.1 Unsupervised Domain Adaptation: Synthetic to Real Logo Detection -- 6.2 Synthesize Images to Avoid Manual Annotation Task -- 6.2.1 Synthetic Logo Images -- 6.3 Domain Alignment Using Entropy Minimization -- 6.3.1 Entropy Minimization -- 6.3.2 Entropy Minimization Maps Using Mid-Level Feature from Synthetic to Real Logo Images -- 6.4 Experiments and Results -- 6.4.1 Datasets -- 6.4.2 Implementation Details -- 6.5 Summary -- 6.6 Discussion and Future Recommendations -- References.