Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture
The judgment of facade styles is an important part of the sustainable utilization and restorative process of historical architectures. Contemporary Harbin needs the help of modern architectural facade forms in the planning of the famous historic city, especially with the facade renovation of old architectures with non-cultural heritage. This paper discusses the possibility of applying advanced image recognition algorithms to the classification of the modern Harbin architectural facade styles and argues that the keys to the classification and positioning of the styles are the forms, the details, and the decorative patterns of the architectural facades, together with the deformation and the quantitative variation factors of the facade decoration symbols. Based on the conventional classification method, the facade styles of Harbin modern architecture were divided into 12 categories after data analysis. To better capture the overall structure information and the style features of the local components in the architectural images, the group convolution and the dilated convolution were added into the ResNet model, and then, the improved channel attention mechanism was introduced to construct a novel CA-MSResNet model. The CA-MSResNet model could more accurately identify the morphological elements and the style categories of the architectures, and the average accuracy reached 87.5%. These techniques, with their promising results, are expected to be further applied in the future research on the sustainable utilization and renovation of Harbin modern architecture.
Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture
The judgment of facade styles is an important part of the sustainable utilization and restorative process of historical architectures. Contemporary Harbin needs the help of modern architectural facade forms in the planning of the famous historic city, especially with the facade renovation of old architectures with non-cultural heritage. This paper discusses the possibility of applying advanced image recognition algorithms to the classification of the modern Harbin architectural facade styles and argues that the keys to the classification and positioning of the styles are the forms, the details, and the decorative patterns of the architectural facades, together with the deformation and the quantitative variation factors of the facade decoration symbols. Based on the conventional classification method, the facade styles of Harbin modern architecture were divided into 12 categories after data analysis. To better capture the overall structure information and the style features of the local components in the architectural images, the group convolution and the dilated convolution were added into the ResNet model, and then, the improved channel attention mechanism was introduced to construct a novel CA-MSResNet model. The CA-MSResNet model could more accurately identify the morphological elements and the style categories of the architectures, and the average accuracy reached 87.5%. These techniques, with their promising results, are expected to be further applied in the future research on the sustainable utilization and renovation of Harbin modern architecture.
Application of Intelligent Technology in Facade Style Recognition of Harbin Modern Architecture
Linlin Shan (Autor:in) / Long Zhang (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
The Protection and Utilization of Daowai Characteristic Building Facade in Harbin
| British Library Conference Proceedings | 2012
The Protection and Utilization of Daowai Characteristic Building Facade in Harbin
| Trans Tech Publications | 2012
Saint Sophia Cathedral - Harbin Architecture Hall
| British Library Online Contents | 1998