Research on Brand Image Recognition Algorithm Based on Computer Vision Technology in Digital Marketing

This paper proposes an innovative computer vision brand image recognition algorithm based on deep learning, aiming to improve the automatic recognition accuracy of brand logos and advertisements through efficient image recognition technology. The algorithm combines the CNN structure to extract features of brand images, recognize logos and analyze advertising content, solving the recognition limitations of traditional algorithms in complex backgrounds and different lighting conditions of brand images. This paper constructs a large dataset containing multiple brand images, and trains and tests them through deep learning frameworks (such as TensorFlow and PyTorch). Experimental results show that the proposed algorithm surpasses traditional image recognition methods in terms of brand recognition accuracy, recall rate and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\mathbf{F 1}$</tex>-score. In multiple brand image recognition tasks, the algorithm shows high robustness when processing images with different angles, different lighting and complex backgrounds. Specifically, the new algorithm improves accuracy by 12 %, recall rate by 8 %, and <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$F 1$</tex>-score by 9 %. In addition, this paper also applies the algorithm to advertising analysis and brand communication effect evaluation in digital marketing, and optimizes marketing strategies through realtime feedback mechanism. Experiments show that the algorithm has strong practicality and commercial value in a variety of marketing environments.