Digital Innovation in Environmental Art Design: The Combination of CAD and Multimodal Fusion Technology.

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Bibliographic Details
Title:	Digital Innovation in Environmental Art Design: The Combination of CAD and Multimodal Fusion Technology.
Authors:	Mei Bai¹ 29020@hnzj.edu.cn
Source:	Computer-Aided Design & Applications. 2025 Special Issue, Vol. 22, p92-104. 13p.
Subjects:	Digital technology, Ecological art, Computer-aided design, Error rates, Feature extraction
Abstract:	This article aims to explore digital innovation in environmental art design (EAD), especially the combined use of computer-aided design (CAD) and multimodal fusion technology. To achieve this goal, this study designed and implemented a series of experiments using a high-performance computing environment and rich EAD data sets for model training and feature detection. The results show that the proposed method has high accuracy in environmental art feature detection, with an accuracy rate of 92.5%. Among 1000 test samples, the model correctly extracts the features of 925 samples, with an error rate of only 7.5%. The sample extraction time for feature monitoring results has been reduced by 15 seconds compared to traditional methods. This has greatly improved the efficiency of feature monitoring. In the field of experimental results, the evaluation effect of digital quantitative analysis has an average score of 9.2 in the process of analyzing experimental results, which has a certain efficiency in the multimodal fusion process of method technology. This further proves the digital multimodal technology fusion analysis method. [ABSTRACT FROM AUTHOR]
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Database:	Engineering Source

Description
Abstract:	This article aims to explore digital innovation in environmental art design (EAD), especially the combined use of computer-aided design (CAD) and multimodal fusion technology. To achieve this goal, this study designed and implemented a series of experiments using a high-performance computing environment and rich EAD data sets for model training and feature detection. The results show that the proposed method has high accuracy in environmental art feature detection, with an accuracy rate of 92.5%. Among 1000 test samples, the model correctly extracts the features of 925 samples, with an error rate of only 7.5%. The sample extraction time for feature monitoring results has been reduced by 15 seconds compared to traditional methods. This has greatly improved the efficiency of feature monitoring. In the field of experimental results, the evaluation effect of digital quantitative analysis has an average score of 9.2 in the process of analyzing experimental results, which has a certain efficiency in the multimodal fusion process of method technology. This further proves the digital multimodal technology fusion analysis method. [ABSTRACT FROM AUTHOR]
ISSN:	16864360
DOI:	10.14733/cadaps.2025.S3.92-104