Ziquan Liu

Corridor management—such as reliance on manual warning zone delineation and inconsistent boundaries—this paper proposes 3DSim-WZD, an automatic ground-level warning zone detection method based on 3D simulation data augmentation. Guided by "interpretable geometric priors combined with deep learning regression," the framework integrates four modules: parametric simulation generation, simulation-to-real transfer, boundary vertex regression, and voltage-level-based expansion. Specifically, parametric virtual scenes are constructed in Unity3D to automatically derive accurate vertex labels. The open-source Stable Diffusion framework, combined with ControlNet and LoRA, is employed for sim-to-real style transfer to reduce domain gaps. Furthermore, directional detection convolutional kernels are incorporated into the YOLO12m backbone to enhance sensitivity to transmission structures. Finally, safety clearance distances are mapped according to voltage levels for regulatory-compliant warning zones. Evaluated on a dataset of 5,000 simulated and 300 real samples, the method achieves a mIoU of 91.2% and an inference speed of 46.8 FPS, demonstrating significant potential for large-scale deployment.