建筑颜色识别算法研究——基于卷积神经网络和图像识别技术

Research on the Building Color Recognition Algorithm-Based on Convolutional Neural Network and Image Recognition Technology

随着信息技术的不断发展,人工智能技术逐渐用于城市建筑色彩规划中。文章提出一种利用卷积神经网络和图像识别技术中的K均值聚类算法的建筑颜色识别算法,并将算法用于建筑色彩规划平台。该算法可识别图像中的建筑物,划分为建筑物和全景图两类进行颜色提取和匹配。首先,通过在深度学习上训练卷积神经网络识别和分割建筑物。随后,K均值算法从分割的建筑物图像中提取颜色。对提取的建筑物类别、颜色、文字信息进行分析,得到建筑物与全景图的对比分析结果。基于文章算法的建筑色彩规划平台有助于智慧城市规划中配色方案的提取和呈现,为未来城市色彩的发展提供新的指导。

With the continuous development of information technology,artificial intelligence technology is gradually used in urban architectural color planning'enhancing the visual experience of the living environment.This paper proposes a building color recognition algorithm that utilizes convolutional neural networks(CNNs)and K-means clustering algorithm.This method can identify and classify buildings in images into two categories:buildings and panoramas for color extraction and matching.Firstly,the process involves training a CNN on deep learning so that buildings can be distinguished and segmented.Subsequently,the K-means algorithm extracts colors from the segmented building images.Analyze the extracted building category,color,and text information to obtain the comparative analysis results of the building and the panorama.It can contribute to the extraction and presentation of color schemes in smart city planning,providing valuable insights into the development of future urban colors.