Indoor space intelligent design method based on improved Resnet neural network

In today's rapidly developing era, intelligent indoor design can not only improve the efficiency and comfort of space use, but also bring new experiences to life and work. However, intelligent design of indoor spaces currently lacks the ability to extract features, and the quality and diversity of generated images are limited. To improve the effectiveness of intelligent indoor space design, in terms of recognition model, the MobileNetV3 model is adopted and a simple parameter free attention mechanism is taken to improve it. In the segmentation model, the study adopts a positional segmentation object model and improves its residual network. In the intelligent design of indoor spaces, an intelligent image generation model based on generative adversarial networks is designed, which includes a three-level generator. The recognition accuracy of the recognition model was 95.98%, which outperformed comparison models. Themaximum accuracy of the segmentation model was 98.36%. The minimum time required to design an intelligent image generation model was 13.55 s, and the rationality, aesthetics, and innovation scores were all better than the comparison models. The recognition, segmentation, and intelligent image generation models can provide support for intelligent design of indoor spaces. The contribution of the research is to construct an integrated indoor space intelligent design method of 'recognition segmentation generation', which improves the effectiveness of indoor space intelligent design.