Metaverse Meets Intelligent Transportation System: An Efficient and Instructional Visual Perception Framework

The combination of the Metaverse and intelligent transportation systems (ITS) holds significant developmental promise, especially for visual perception tasks. However, the acquisition of high-quality scene data poses a challenging and expensive endeavor. Meanwhile, the visual disparity between the Metaverse and the physical world poses an impact on the practical applicability of the visual perception tasks. In this paper, a Metaverse Intelligent Traffic Visual Framework, MITVF, is developed to guide the implementation of visual perception tasks in the physical world. Firstly, a two-stage metadata optimization strategy is proposed that can efficiently provide diverse and high-quality scene data for traffic perception models. Specifically, an element reconfigurability strategy is proposed to flexibly combine dynamic and static traffic elements to enrich the data with a low cost. A diffusion model-based metadata optimization acceleration strategy is proposed to achieve efficient improvement of image resolution. Secondly, a Meta-Physical adaptive learning method is proposed, and further applied to visual perception tasks to compensate for the visual disparity between the Metaverse and the physical world. Experimental results show that MITVF achieves a <inline-formula> <tex-math notation="LaTeX">$10\times $ </tex-math></inline-formula> acceleration in optimization speed, ensuring the image quality and reconstructing diverse. Further, MITVF is applied to the traffic object detection task to verify the effectiveness and validity. The performance of the model trained with 5k real data exceeded that of the model trained with 200k real data, with AP50 reaching 67.7%.