Abstract Logo classification is crucial in various applications, including brand monitoring, copyright protection, and digital forensics. Traditional computer vision techniques face significant limitations, particularly in handling scale variations, occlusions, and background clutter. While deep learning models, particularly convolutional neural networks (CNNs), offer superior solutions, they often come with high computational costs, posing challenges for real-time deployment. This paper introduces LogoXpertNet, a lightweight deep learning architecture specifically designed for efficient logo classification. The key innovations of LogoXpertNet include: (1) a modified MobileNetV3 backbone enhanced with bottleneck and squeeze-and-excitation (SE) blocks for efficient feature extraction; (2) a novel cross-layer feature fusion (CLFF) module that improves feature integration across network depths; (3) a newly proposed hierarchical squeeze-excitation spatial attention block (HSE-SAB) that dynamically attends to both spatial and channel-wise features; and (4) a feature-aware convolution block attention module (FA-CBAM) that uniquely fuses spatial and frequency-domain information for refined logo attention. Extensive experiments on the evaluated benchmark datasets, including FlickrLogos-32, BelgaLogos, and WebLogo-2M, show that LogoXpertNet achieves strong classification performance while maintaining low computational overhead. Because the reported accuracies approach saturation on these benchmarks, the results should be interpreted in the context of dataset characteristics, split construction, and metric definition rather than as evidence of universal performance across all real-world logo-recognition scenarios. LogoXpertNet provides an efficient and practical framework for benchmark logo classification and offers a promising basis for future validation under more challenging real-world conditions.
Mumtaz et al. (Thu,) studied this question.