Traffic Signal Detection and Recognition Based on Faster R-CNN Model
With the continuous development of urban transportation and the advancement of intelligent processes,the accurate detection and recognition of traffic signals become crucial for improving traffic safety and efficiency.Based on the conventional recognition algorithm,this paper proposes to use the Faster R-CNN algorithm to achieve efficient and accurate detection and recognition of traffic signals.The data quality and accuracy are ensured by manually labeled datasets with data preprocessing and labeling.The Faster R-CNN model is established based on the PyTorch framework and is trained to ensure model convergence.In terms of model evaluation,the model performance is comprehensively evaluated using indicators such as accuracy and recall rate.The results show that the proposed model performs well in the traffic signal detection and recognition tasks,with a prediction accuracy of more than 90％,which has positive significance for developing traffic management and intelligent transportation systems.
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