The use of graphic design elements in commercial architectural design in the context of intelligence
In this paper, firstly, in the context of intelligence, using techniques such as hierarchical features, local perceptual field, weight sharing, and spatial subsampling make the convolutional neural network with representational learning ability, certain translation, scaling, and deformation invarian...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns.2023.2.00392 |
| _version_ | 1797340720432939008 |
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| author | Song Yimin |
| author_facet | Song Yimin |
| author_sort | Song Yimin |
| collection | DOAJ |
| description | In this paper, firstly, in the context of intelligence, using techniques such as hierarchical features, local perceptual field, weight sharing, and spatial subsampling make the convolutional neural network with representational learning ability, certain translation, scaling, and deformation invariance, which can accurately extract the features of target objects in graphic design elements. Next, the network structure, loss function, and optimization methods of MaskR-CNN are analyzed, and the MaskR-CNN convolutional neural network is implemented based on the TensorFlow framework, and the network is applied to the extraction of buildings from graphic design elements. Then the borrowed landscape technique in graphic design is used to enrich the combination form of elements of architectural design through the mirror effect of water landscape. The selected IALD dataset is composed of 180 data images and 180 corresponding labeled images, and the effect of high-resolution building extraction of graphic design elements based on the MaskR-CNN model is verified by simulation experiments. The simulation results show that Mask R-CNN improves the accuracy of building extraction from graphic design elements by 5.11%, mAP also improves by about 10.09%, and the inference time is less than 1 second; Mask R-CNN has good robustness and accuracy for building extraction from graphic design elements. This study has important research significance for the depth and development of the commercial architectural design. |
| first_indexed | 2024-03-08T10:08:17Z |
| format | Article |
| id | doaj.art-004356abd9ae4e8fb90256c821ba3f7a |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-08T10:08:17Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-004356abd9ae4e8fb90256c821ba3f7a2024-01-29T08:52:32ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns.2023.2.00392The use of graphic design elements in commercial architectural design in the context of intelligenceSong Yimin0Anhui University of Technology, Ma’anshan, Anhui, 243032, ChinaIn this paper, firstly, in the context of intelligence, using techniques such as hierarchical features, local perceptual field, weight sharing, and spatial subsampling make the convolutional neural network with representational learning ability, certain translation, scaling, and deformation invariance, which can accurately extract the features of target objects in graphic design elements. Next, the network structure, loss function, and optimization methods of MaskR-CNN are analyzed, and the MaskR-CNN convolutional neural network is implemented based on the TensorFlow framework, and the network is applied to the extraction of buildings from graphic design elements. Then the borrowed landscape technique in graphic design is used to enrich the combination form of elements of architectural design through the mirror effect of water landscape. The selected IALD dataset is composed of 180 data images and 180 corresponding labeled images, and the effect of high-resolution building extraction of graphic design elements based on the MaskR-CNN model is verified by simulation experiments. The simulation results show that Mask R-CNN improves the accuracy of building extraction from graphic design elements by 5.11%, mAP also improves by about 10.09%, and the inference time is less than 1 second; Mask R-CNN has good robustness and accuracy for building extraction from graphic design elements. This study has important research significance for the depth and development of the commercial architectural design.https://doi.org/10.2478/amns.2023.2.00392maskr-cnntensorflow frameworkloss functioncommercial architectural designaccuracyrobustness.65d17 |
| spellingShingle | Song Yimin The use of graphic design elements in commercial architectural design in the context of intelligence Applied Mathematics and Nonlinear Sciences maskr-cnn tensorflow framework loss function commercial architectural design accuracy robustness. 65d17 |
| title | The use of graphic design elements in commercial architectural design in the context of intelligence |
| title_full | The use of graphic design elements in commercial architectural design in the context of intelligence |
| title_fullStr | The use of graphic design elements in commercial architectural design in the context of intelligence |
| title_full_unstemmed | The use of graphic design elements in commercial architectural design in the context of intelligence |
| title_short | The use of graphic design elements in commercial architectural design in the context of intelligence |
| title_sort | use of graphic design elements in commercial architectural design in the context of intelligence |
| topic | maskr-cnn tensorflow framework loss function commercial architectural design accuracy robustness. 65d17 |
| url | https://doi.org/10.2478/amns.2023.2.00392 |
| work_keys_str_mv | AT songyimin theuseofgraphicdesignelementsincommercialarchitecturaldesigninthecontextofintelligence AT songyimin useofgraphicdesignelementsincommercialarchitecturaldesigninthecontextofintelligence |