Abstract
With rapid development of digital age, the brand LOGO is an essential visual identity for enterprises, and its design, identification, and analysis have become a research hotspot in computer vision and image processing. Traditional manual recognition methods are inefficient, and it isn’t easy to meet needs of large-scale, rapid, and accurate. Therefore, automatic recognition and analysis technology based on computer vision and image processing emerged. This study aims to explore an efficient and accurate automatic identification and analysis method of brand LOGO design to improve the intelligent level of brand management. Firstly, the research constructs a large-scale brand LOGO database, covering LOGO images of various styles, industries, and complex backgrounds. A complete LOGO recognition and analysis system is designed to use convolutional neural networks (CNN) as the primary model in deep learning, combined with key technologies such as image preprocessing, feature extraction, and classification recognition. In the experimental stage, through training and testing of 10,000 LOGO images, the results show that recognition accuracy rate of the system reaches 95.8%, which is nearly 15 percentage points higher than that of traditional methods. At same time, in analyzing LOGO design elements, such as color, shape, and texture, the system’s accuracy rate reached 92.3%, which provides strong data support for brand design. The influence of different network structures and parameter adjustments on the recognition effect is also discussed, and the model’s performance is optimized by cross-validation. This study not only realizes the automatic identification and analysis of brand LOGO but also provides new ideas and solutions for intelligent applications in related fields.
Get full access to this article
View all access options for this article.