Real-Time Blowing-Snow Visibility Detection and Variable Message Sign Coordination for Highway Operations

Accurate and real-time visibility estimation under blowing-snow conditions is crucial for ensuring roadway safety and maintaining operational continuity in cold-region transportation networks. This study proposes a lightweight visibility detection framework based on an improved YOLOv8 (You Only Look Once v8) architecture, termed YOLO-SDlite. Through module-level modifications and structural simplification, YOLO-SDlite achieves high classification accuracy while substantially reducing computational cost. In particular, we introduce a CSPDW (cross-stage partial (CSP) connections + depthwise separable convolution) module that uses depthwise convolution (DWConv) for local feature extraction and channel alignment, preserving CSP characteristics while cutting complexity. We also incorporate an improved omnidimensional dynamic convolution (ODConv_Lite), bringing multidimensional dynamic attention into the feature extractor to adaptively recalibrate along the spatial domain, input channels, output channels, and convolution kernels. Experimental results demonstrate that the model effectively grades visibility levels in real time, supporting reliable performance on resource-constrained roadside devices. Specifically, it attains a 68.8% reduction in parameters and 66.7% reduction in model size compared with the baseline YOLOv8, while sustaining 93% classification accuracy. Building upon these perception capabilities, a coordinated variable message sign (VMS) control strategy is designed to translate model outputs into actionable traffic management measures, including dynamic speed limits, warnings, and closure operations. The integrated framework bridges environment perception and operational decision-making, providing a scalable solution for intelligent, weather-responsive traffic management.