Brand Logos Recognition System Using Image Processing for Food and Beverage Brands

Abstract

This study investigates the development of a Brand Logo Recognition (BLR) system employing Convolutional Neural Networks (CNNs), specifically designed for the food and beverage industry in Ipoh. Accurate logo recognition is vital for businesses to strengthen brand identity, monitor consumer engagement, and mitigate the misuse of counterfeit logos. Existing systems often encounter challenges related to variations in logo design, image quality, and lighting conditions. To address these issues, the research adopts a hybrid methodology that integrates the Machine Learning Life Cycle and the Software Development Life Cycle (SDLC), utilizing an iterative Agile development framework. The system incorporates CNN models for feature extraction and classification, complemented by Single Shot Detector (SSD) algorithms for object detection. A curated dataset of food and beverage logos underwent preprocessing techniques, including resizing, normalization, and augmentation, to enhance the model’s generalization capabilities. Empirical results demonstrate high accuracy in detecting and classifying logos across diverse conditions, underscoring the effectiveness of the CNN-SSD architecture. The proposed system offers practical applications for marketing analytics and consumer research, empowering local businesses to refine branding strategies and improve customer engagement. Future research directions include the exploration of multi-label classification, real-time processing, and the integration of advanced methodologies, such as generative adversarial networks (GANs), for counterfeit logo detection. This study emphasizes the transformative potential of AI-driven logo recognition systems in revolutionizing marketing practices and supporting small and medium-sized enterprises (SMEs).