Adaptive Logo Recognition System
- DOI
- 10.2991/978-94-6463-471-6_59How to use a DOI?
- Keywords
- Convolutional Neural Network; Feature Extraction; Feature Fusion; Logo detection
- Abstract
Logo Detection plays a vital role in different applications such as brand checking, copyright assurance, and visual look. In this paper, we explore the viability of convolutional Neural Networks (CNNs) and combination procedures for Logo Detection. We used three pre-trained CNN models VGG-16, DenseNet-201, and ResNet-50. Each model was applied independently on two datasets accomplishing the most extreme accuracies of 92.5% with DenseNet-201, 92.07% with VGG-16, and 90.45% with ResNet-50. Besides, we investigated the fusion model-VDeRe-24 to combine forecasts from these models, coming about in a combined result with an accuracy 94%. Our experimental findings highlight the potential fusion CNN model in making strides in Logo Detection accuracy, comparing with the state-of-the-art techniques for brand checking and copyright, assurance applications.
- Copyright
- © 2024 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - M. Rajababu AU - M. D. V. S. Lakshmi AU - P. Sai Sudha AU - K. H. V. Ravi Chandra AU - V. Mohan Sai Venkat PY - 2024 DA - 2024/07/30 TI - Adaptive Logo Recognition System BT - Proceedings of the International Conference on Computational Innovations and Emerging Trends (ICCIET- 2024) PB - Atlantis Press SP - 609 EP - 617 SN - 2352-538X UR - https://doi.org/10.2991/978-94-6463-471-6_59 DO - 10.2991/978-94-6463-471-6_59 ID - Rajababu2024 ER -