Intelligent color matching color wheel based on RGB color sensor and optical characteristic analysis

With the rapid advancement of artificial intelligence and big data, color data analysis has become essential in intelligent design and application. Traditional Red Green Blue (RGB) intelligent color matching palette, which struggle to meet the real-time and accuracy requirements for complex lighting conditions and personalized needs. Therefore, the study innovatively designed an intelligent spectral response color palette model that integrates an improved Convolutional Neural Network (CNN) and a Generative Adversarial Network (GAN). By optimizing spectral feature responses through a SE module, Finally, deep learning technology is used to improve the accuracy of color recognition and color matching scheme generation. Experiments show that the model demonstrates faster convergence rates on datasets. When the sample size is 500, the CNN-GAN achieves a color harmony accuracy rate of 95.12% on the Adobe Color-10K dataset and 94.86% on the Pantone Fashion dataset. The results show that the model accurately collects the optical characteristics of RGB color sensor and efficiently processes the deep learning technology, which significantly improves the diversity and accuracy of color matching scheme, and provides low error and high reliability color matching support for creative design, industrial application and other scenarios.