6 research outputs found
Deep Learning for Logo Detection: A Survey
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
FashionLOGO: Prompting Multimodal Large Language Models for Fashion Logo Embeddings
Logo embedding plays a crucial role in various e-commerce applications by
facilitating image retrieval or recognition, such as intellectual property
protection and product search. However, current methods treat logo embedding as
a purely visual problem, which may limit their performance in real-world
scenarios. A notable issue is that the textual knowledge embedded in logo
images has not been adequately explored. Therefore, we propose a novel approach
that leverages textual knowledge as an auxiliary to improve the robustness of
logo embedding. The emerging Multimodal Large Language Models (MLLMs) have
demonstrated remarkable capabilities in both visual and textual understanding
and could become valuable visual assistants in understanding logo images.
Inspired by this observation, our proposed method, FashionLOGO, aims to utilize
MLLMs to enhance fashion logo embedding. We explore how MLLMs can improve logo
embedding by prompting them to generate explicit textual knowledge through
three types of prompts, including image OCR, brief captions, and detailed
descriptions prompts, in a zero-shot setting. We adopt a cross-attention
transformer to enable image embedding queries to learn supplementary knowledge
from textual embeddings automatically. To reduce computational costs, we only
use the image embedding model in the inference stage, similar to traditional
inference pipelines. Our extensive experiments on three real-world datasets
demonstrate that FashionLOGO learns generalized and robust logo embeddings,
achieving state-of-the-art performance in all benchmark datasets. Furthermore,
we conduct comprehensive ablation studies to demonstrate the performance
improvements resulting from the introduction of MLLMs
Deep Logo Authenticity: Leveraging R-CNN for Counterfeit Logo Detection in E-commerce
In the rapidly evolving realm of electronic commerce, ensuring the accuracy and authenticity of merchandise assumes paramount importance in maintaining consumer trust and platform reliability. One of the prominent challenges encountered within this particular domain revolves around the pervasive prevalence of counterfeit products, often discernible through subtle deviations in brand insignias. This research paper introduces a novel approach to detect counterfeit logos on electronic commerce platforms using Region-based Convolutional Neural Networks (R-CNN). Traditional approaches often rely on manual verification or basic image comparisons, both of which have drawbacks in terms of scalability and consistent accuracy. The methodology utilized in our research capitalizes on the capabilities of deep learning algorithms to precisely identify and classify logos depicted in product images, proficiently distinguishing genuine logos from counterfeit ones with a significant degree of precision. A meticulously curated dataset was compiled, encompassing genuine and counterfeit logos sourced from renowned brands. By means of intensive training, our model demonstrated remarkable aptitude, surpassing the capabilities of contemporary methodologies. The current investigation not only offers a significant contribution to enhancing the security and reliability of electronic commerce platforms, but also establishes the foundation for the advancement of advanced counterfeit detection methodologies within the domain of digital marketplaces
Deep learning for logo detection
We present a deep learning system for automatic logo detection in real world images. We base our detector on the popular framework of FasterR-CNN and compare its performance to other models such as Mask R-CNN or RetinaNet. We perform a detailed empirical analysis of various design and architecture choices and show how these can have much higher influence than algorithmic tweaks or popular techniques such as data augmentation. We also provide a systematic detection performance comparison of various models on multiple popular datasets including FlickrLogos-32, TopLogo-10 and recently introduced QMUL-OpenLogo benchmark, which allows for a direct comparison between recently proposed extensions. By careful optimization of the training procedure we were able to achieve significant improvements of the state of the art on all mentioned datasets. We apply our observations to build a detector to detect logos of the Red Bull brand in online media and images