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

TL;DR
This paper presents a deep learning framework combining logo detection and saliency prediction to quantify brand visibility on packaging, validated through datasets and psychophysical hypotheses.
Contribution
It introduces a novel saliency model with transformers for packaging, integrating logo detection and attention scoring, advancing consumer-centric packaging design analysis.
Findings
Proposed logo detection with YOLOv8 outperforms state-of-the-art models.
Saliency prediction aligns with psychophysical studies on brand visibility.
Brand attention score is robust and correlates with visual elements and positioning.
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.…
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Taxonomy
TopicsConsumer Packaging Perceptions and Trends · Digital Media and Visual Art · Consumer Behavior in Brand Consumption and Identification
MethodsYou Only Look Once · Linear Layer · Average Pooling · *Communicated@Fast*How Do I Communicate to Expedia? · Sigmoid Activation · Global Average Pooling · Convolution · 1x1 Convolution · Efficient Channel Attention · Absolute Position Encodings
