Brand Visibility in Packaging: A Deep Learning Approach for Logo Detection, Saliency-Map Prediction, and Logo Placement Analysis

TL;DR

This work addresses the challenge of quantifying brand logo visibility on packaging by introducing a three-module framework that combines automated logo detection, a text-aware saliency predictor, and a Brand-Attention score to quantify logo prominence. The approach integrates YOLOv8 for robust logo localization with a CNN-Transformer saliency model that fuses image data and text maps via a learnable Fusion Block and efficient-attention transformers. Key contributions include a strong logo-detection pipeline on FoodLogoDet-1500 and LogoDet-3K, a novel saliency model tailored to advertising and packaging contexts, and a dataset for exploring brand-attention hypotheses, including seven newly proposed ideas. The proposed framework enables data-driven packaging design and consumer-centrered branding insights, with potential extensions to video saliency and packaging-generation networks for optimized logo placement and visual impact.

Abstract

In the highly competitive area of product marketing, the visibility of brand logos on packaging plays a crucial role in shaping consumer perception, directly influencing the success of the product. This paper introduces a comprehensive framework to measure the brand logo's attention on a packaging design. The proposed method consists of three steps. The first step leverages YOLOv8 for precise logo detection across prominent datasets, FoodLogoDet-1500 and LogoDet-3K. The second step involves modeling the user's visual attention with a novel saliency prediction model tailored for the packaging context. The proposed saliency model combines the visual elements with text maps employing a transformers-based architecture to predict user attention maps. In the third step, by integrating logo detection with a saliency map generation, the framework provides a comprehensive brand attention score. The effectiveness of the proposed method is assessed module by module, ensuring a thorough evaluation of each component. Comparing logo detection and saliency map prediction with state-of-the-art models shows the superiority of the proposed methods. To investigate the robustness of the proposed brand attention score, we collected a unique dataset to examine previous psychophysical hypotheses related to brand visibility. the results show that the brand attention score is in line with all previous studies. Also, we introduced seven new hypotheses to check the impact of position, orientation, presence of person, and other visual elements on brand attention. This research marks a significant stride in the intersection of cognitive psychology, computer vision, and marketing, paving the way for advanced, consumer-centric packaging designs.

Figures (7)

• Figure 1: Overview of the proposed Brand-Attention method
• Figure 2: The block diagram of the proposed saliency model.
• Figure 3: Architecture of dot-product and efficient attentionshen2021efficient
• Figure 4: Sample images from FoodLogoDet-1500, LogoDet-3K, and SalECI datasets
• Figure 5: Comparison of the saliency maps of different models over SALECI