Brand Visibility in Packaging: A Deep Learning Approach for Logo
  Detection, Saliency-Map Prediction, and Logo Placement Analysis
Alireza Hosseini Kiana Hooshanfar Pouria Omrani Reza Toosi Ramin Toosi Zahra Ebrahimian Mohammad Ali Akhaee
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.