Human perception evaluation system for urban streetscapes based on computer vision algorithms with attention mechanisms

Abstract

AbstractVirtual 3D modeling is widely implemented in urban planning and design. To evaluate urban planning modeling, based on existing computer vision models, this article aims to improve performance in the field of human perception analysis for urban street views. In this study, the PSP module extracts detailed features from recognized objects of different sizes, an attention mechanism is applied to solve the problem of large information differences in pictures, and transfer learning technology is used to expand the model to the field of virtual 3D modeling to extract more representative and universal features, similar to how humans perceive street view information. Finally, we obtain a more objective, stable, and accurate neural network model that imitates human perception. This evaluation model converges within the correct interval on the training and validation datasets compared with an evaluation of virtual modeling by a large number of people.