The Analysis of Industrial Heritage Landscape Design in Tourism Based on Virtual Reality and Convolutional Neural Network
Year: 2025
Type: article
Abstract: To enhance the efficiency and performance of virtual reality systems applied to industrial heritage landscape design, this study introduces an optimization model based on MobileNetV4, accompanied by a thorough experimental evaluation. A series of comparative experiments reveals that this model excels in various critical metrics, including model accuracy, inference speed, model size, and computational complexity. For a dataset comprising 5,000 samples, the model achieves an impressive accuracy of 0.910, an inference speed of 30.787 ms, a compact model size of 5.216 MB, and a computational complexity of 0.177 GFLOPS. Additionally, the contributions of pivotal components to the model's performance were meticulously assessed through ablation studies. Results indicate that the elimination of the depth-separable convolution layer resulted in a slowdown of inference speed to 33.090 ms and an increase in computational complexity to 0.204 GFLOPS.
Authors: Qixin Song, Guoxia Sun
Institution: The Tourism College of Changchun University
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4414268551