With the continuous growth of highway traffic flow, accurate and efficient traffic event detection has become critical for ensuring road safety and smooth operations. A highway traffic event detection method based on the interaction of large and small models was proposed. First, the YOLOv8 small model leverages its efficient object localization mechanism to quickly perform preliminary detection and screening of traffic targets from incoming video frames, accurately identifying potential event regions. Then, a Transformer-based large model conducts in-depth analysis of the candidate regions, achieving precise recognition of traffic event types through global feature extraction and semantic understanding. By combining the small model’s rapid screening with the large model’s detailed analysis, the proposed method balances detection speed and accuracy. Experimental results on a self-built highway traffic event dataset demonstrate that this approach significantly outperforms single-model methods in terms of precision, recall, and other metrics, providing an efficient and reliable technical solution for highway traffic management.