Abstract :

Logo detection has become an essential task in visual recognition for applications such as brand monitoring, copyright management, and marketing analytics. This work presents a deep learning-based approach to accurately detect and classify logos in diverse real-world images. The system leverages convolutional neural networks (CNNs) and state-of-the-art object detection algorithms to handle variations in scale, background, and lighting. A comprehensive dataset with multiple logo classes was used to train and evaluate the model under challenging scenarios. Experimental results demonstrate that the proposed system reliably identifies logos with high precision and recall. Through this study, we conclude that deep learning significantly enhances logo detection performance compared to traditional methods. The research also highlights optimization techniques that boost detection speed without compromising accuracy. Overall, this study contributes to improved automated visual brand recognition