Research on Brand Visual Communication Design Innovation Supported by Computer Vision in the Digital Age
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Abstract
Brand visual communication in the digital environment requires automated recognition, generation, and consistency assessment of large-scale visual materials. This study proposes a computer-vision-supported framework covering brand-logo recognition and optimization, color-system extraction and reconstruction, and visual-element consistency evaluation. A dataset containing 10,000 visual samples from 50 brands is established for logo recognition and element detection, while additional cross-media samples are used for color reconstruction. ResNet-50-based convolutional neural networks are used for logo recognition and design-quality evaluation, a CycleGAN-based model reconstructs brand color systems across digital media, and Faster R-CNN detects visual elements for consistency scoring. Experimental results show that the proposed method achieves high logo-recognition accuracy, improves color-reconstruction satisfaction, and supports automated monitoring of cross-media visual consistency. The framework provides a technical route for image-signal processing, visual feature extraction, and intelligent design-system evaluation.
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