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Indoor space intelligent design method based on improved Resnet neural network.

Jiaying Wu1

  • 1School of Art, Zhengzhou Business University, Zhengzhou, 451200, China. 15039097317@163.com.

Scientific Reports
|December 8, 2025
PubMed
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This study enhances intelligent indoor design by integrating recognition, segmentation, and generation models. The new approach improves feature extraction and image quality for more effective and innovative interior space creation.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Architecture

Background:

  • Intelligent indoor design enhances space utility and user experience but faces limitations in feature extraction and image generation quality.
  • Current intelligent design systems lack robust methods for comprehensive indoor space analysis and creation.

Purpose of the Study:

  • To develop an integrated intelligent indoor space design method combining recognition, segmentation, and generation.
  • To improve the effectiveness, quality, and diversity of intelligent indoor space design.

Main Methods:

  • An improved MobileNetV3 model with an attention mechanism was used for feature recognition.
  • A positional segmentation object model with an enhanced residual network was employed for segmentation.
  • A generative adversarial network (GAN) with a three-level generator was designed for intelligent image generation.
Keywords:
DesignMobileNetV3Resnet neural networkSOLOv2SimAMSpace

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Main Results:

  • The recognition model achieved 95.98% accuracy, surpassing comparison models.
  • The segmentation model reached a maximum accuracy of 98.36%.
  • The image generation model demonstrated superior rationality, aesthetics, and innovation, with a minimum design time of 13.55 seconds.

Conclusions:

  • The integrated 'recognition-segmentation-generation' method significantly enhances intelligent indoor space design.
  • The developed models provide robust support for creating efficient, comfortable, and innovative indoor environments.
  • This research contributes a novel framework for advancing the field of intelligent architectural and interior design.