Interior Innovation Design Using ResNet Neural Network and Intelligent Human–Computer Interaction

Abstract

This study aims to optimize the interior design generation process while enhancing personalization and efficiency through the development of a hybrid Residual Network-Human-Computer Interaction (ResNet-HCI) framework. It employs Residual Network (ResNet) neural networks, which leverage residual learning to improve training stability and feature extraction capabilities in deep networks. This allows for efficient feature reuse and optimization of model performance. Additionally, Human-Computer Interaction (HCI) technologies, such as voice commands, gesture control, and Virtual Reality/Augmented Reality (VR/AR), are integrated to enhance user interaction with the design system, thereby improving the intelligence and personalization of the interior design workflow. The Large-Scale Scene Understanding dataset is used for model training to evaluate system performance under varying training steps, hyperparameter configurations, and noise conditions. The experimental results show the following: 1) Significant performance variations are observed across different models under conditions such as increased training iterations, noise interference, and design scoring. In the iteration experiment, model performance generally improves with more training steps. ResNet50 consistently outperforms other models, achieving an F1 score of 0.935 after 20 iterations, demonstrating exceptional feature learning and stability. In the noise robustness analysis, ResNet50 and ResNeXt show minimal performance degradation under Gaussian noise, indicating strong noise robustness. 2) Regarding interior design scoring, hybrid layouts generated using ResNet models combined with HCI technologies excel in multiple dimensions, achieving the highest overall satisfaction scores and emerging as the optimal design solutions. These findings validate the exceptional performance and versatility of ResNet50 and ResNeXt in both deep learning and HCI applications. This study provides both theoretical and practical support for the intelligent transformation of the interior design field, while also offering insights into broader applications in creative industries.