Intelligent automobile traffic sign detection based on context feature aggregation

Traffic sign detection technology plays a key role in the field of intelligent vehicles and is an important foundation for ensuring road safety and improving environmental perception capabilities. To improve the detection accuracy and real-time performance of traffic signs in complex environments, this study proposes an intelligent vehicle traffic sign detection model based on contextual feature aggregation. This model enhances its ability to focus on key region features by introducing horizontal channel attention and guides the fusion of multi-scale contextual semantic information through multi cavity spatial pyramid pooling. The experiment showed that the proposed model significantly outperformed the compared algorithms in accuracy, F1 value, and frame rate. Among them, the highest accuracy was 0.93, the F1 value was 0.90, the average processing time was 141 milliseconds, and the single frame floating-point operation was reduced to 30.7G. In further testing under different data volumes and traffic scenario tasks, the model maintained stable performance in sample expansion and achieved a good balance between recognition rate and speed. In the ablation experiment, each module had a positive improvement effect on performance, especially in small target recognition and complex background suppression, with significant advantages. Research has shown that the constructed model has high detection accuracy, good computational efficiency, and stable generalization ability, and is suitable for multi class traffic sign recognition tasks in intelligent driving systems.