Abstract
With the modernization of cities, public sculptures are constantly being designed and constructed. The artistic form and image expression effect of sculpture based on intelligent and parametric design needs to be designed and developed to guide and assist the construction of sculpture. This paper applies the NAS architecture search method to explore the field of image expression effect models. Through the end-to-end search of the experiment designed in this paper, the separable convolution lightweight design is used, and the new model AestheticNet is used to predict the image form effect score distribution. Secondly, this paper proposes optimization strategies combining image expression effect theory and convolutional neural network, including improvement of Loss function self-weighted Loss, two-dimensional Attention mechanism – introduction of CBAM, and adaptive pooling layer. Optimization of several aspects, such as adaptive input. Finally, the validation set is compared with other existing image-morphological effect model algorithms, which proves the effectiveness of the customized search scheme. It demonstrates the efficacy of the AestheticNet model compared to other algorithms by validating its prediction of public sculpture image form effect ratings. The artistic form using intelligent and parametric design methodologies may improve. Image expression of sculptures may be enhanced by applying the image form effect model, which should be pervasive. We can use it to intelligently and parametrically guide the design and manufacture of sculptures.