Logo-SSL: Self-supervised Learning with Self-attention for Efficient Logo Detection

Logo detection is pivotal in various real-world applications, such as trademark protection, advertising analysis, image search, and copyright enforcement, enabling companies to protect their brand identity, gauge market influence, and enhance search and recommendation systems. However, traditional methods are constrained by their heavy reliance on manually annotated large-scale logo datasets, a labour-intensive, time-consuming process, and prone to variability. This paper introduces Logo-SSL, an innovative approach integrating self-supervised learning and self-attention mechanisms to advance logo detection. By leveraging unsupervised data for pre-training and incorporating self-attention, Logo-SSL transcends the limitations of traditional methods, achieving comparable logo detection accuracy and efficiency without the need for manual annotation. Experimental results, benchmarked against several other SSL pre-trained models, validate the hypotheses that SSL can attain performance similar to supervised learning and that training on logo-specific datasets outperforms general object datasets like ImageNet. Logo-SSL reduces labour costs and time and offers a practical, cost-effective, and scalable logo detection approach to more extensive and diverse real-world logo detection applications.