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.

本研究介绍了一种全面评估包装设计中品牌标志符关注度的框架，该框架包括使用YOLOv8进行标志符检测、使用新型显著性预测模型建模用户视觉关注度以及将标志符检测与显著性地图生成结合以提供全面的品牌关注度评分。通过比较与现有模型的标志符检测和显著性地图预测，结果显示了所提方法的优越性，并对品牌能见度相关的心理物理学假设进行了验证。研究在认知心理学、计算机视觉和市场营销的交叉领域取得了重要进展，为先进的以消费者为中心的包装设计铺平了道路。

包装中的品牌可见性：徽标检测、显著性图预测和徽标位置分析的深度学习方法