Real-Time Assistive System Integrating Geometric Topology Analysis and State-Adaptive Warning Logic for the Visually Impaired

Traditional white canes offer a limited perception range, whereas end-to-end visual models face challenges in real-time deployment on edge devices. To address these limitations, this paper proposes a lightweight real-time assistive system that integrates geometric topology reconstruction with state-adaptive warning logic. The system utilizes YOLOv9 to extract discrete semantic primitives of tactile paving. It constructs a dual-branch perception framework based on Median Absolute Deviation and the Minimum Spanning Tree algorithm to analyze the topological structure of tactile paving. For complex intersections characterized by warning indicators, a one-dimensional connectivity clustering algorithm based on longitudinal topology is proposed. It generates accurate macroscopic feasible directional prompts under field-of-view boundary constraints. Additionally, a hierarchical scheduling framework dynamically orchestrates scenario-specific finite state machines to enable continuous dynamic interaction across typical high-risk scenarios. Evaluated on a custom real-world dataset, the system achieves a 95.21% frame-level comprehensive accuracy for straight-path deviation correction and intersection directional prompting. Dynamic temporal stress tests confirm the temporal stability and logical coherence of state transitions. Furthermore, latency evaluations demonstrate the logic layer’s minimal computational overhead, proving its theoretical feasibility for real-time edge deployment. This approach provides an effective, low-latency solution for delivering directional prompts and hazard warnings to visually impaired users.