Application of Relying on 3D Modeling Technology in the Design of Urban Sculpture
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Abstract
Urban sculptures have long been a reflection of the cultural and historical identity of a city, serving as both artistic expressions and landmarks. The application of deep learning in the context of sculptures within urban areas presents an intriguing intersection of art and technology. Deep learning algorithms, particularly those related to computer vision, have the capacity to analyze and understand the intricate details of sculptures. This paper presented a efficient 3D-AWE (3D-Weighted Architecture Estimation) in the analysis of urban sculptures. In an era where the preservation and interpretation of cultural heritage are of paramount importance, this study investigates the potential of advanced technology to enhance understanding of artistic and historical artifacts within urban environments. The proposed 3D-AWE model uses the weighted estimation with computation of the pixels in the scupturs. Additionally, the proposed 3D-AWE model uses the min-max estimation model for the computation of the features in the images. With the estimated features the deep learning through weighted model for the analysis of the sculptures. The research focuses on the accurate classification of urban sculptures into specific styles and periods, such as Baroque, Renaissance, Modernist, Abstract, Ancient, and Contemporary. Utilizing precision, recall, F1 Scores, and overall accuracy, the study highlights the model's ability to minimize errors and provide reliable categorization. Furthermore, the application of 3D-AWE for feature extraction reveals quantifiable representations of sculpture attributes, offering valuable insights for sculpture categorization, similarity analysis, and the automated management of museum collections. The implications of these findings extend to art history, cultural heritage preservation, and urban planning, underscoring the significance of advanced technology in efforts to safeguard and interpret cultural legacy.
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