Intelligent Color System Based on Image Recognition Algorithm

Traditional color recognition methods are subjective and easily influenced by external factors such as light and materials, which often lead to low recognition accuracy and long recognition time. In order to solve these problems, this paper proposes an intelligent color system based on Convolutional Neural Network (CNN). The system adopts machine vision technology and realizes high-precision automatic color recognition through CNN. In this study, a high-resolution camera is used to capture images, and the contrast, brightness and noise are adjusted to improve the image quality. Then the image is converted from RGB color space to HSV color space, and the color information is expressed more intuitively and the color characteristics are highlighted by using the three parameters of hue, saturation and brightness in HSV color space. In the feature extraction stage, different colors are distinguished by setting specific hue, saturation and brightness range. Using the function of ‘cv2.inRange ()’ in OpenCV library, the target color is extracted according to the defined color range. In addition, this paper also uses CNN to effectively classify images by mapping from original pixels to advanced abstract features. The experimental results show that the intelligent color system based on CNN achieves 99.3% recognition accuracy and 114ms-292ms recognition time, which is significantly better than the system based on Transformer and RNN. Combined with the experimental data, the conclusion of this study verifies the effectiveness and potential of intelligent color system based on CNN in automatic color recognition, and provides strong technical support for color-related industries.