DATASET AND IMPROVED YOLOV7 FOR TEXT-BASED TRAFFIC SIGN DETECTION

Abstract. Traffic sign detection is an important part of autonomous driving technology, and it is also important to have a large-scale dataset applicable to Chinese traffic scenarios. The article proposes a text-based self-labelled traffic sign dataset which consists of 3153 images, of which 2903 images are used for training and 250 images are used for validation. And an improved YOLOv7 algorithm is provided that incorporates the BiFormer attention mechanism into the YOLOv7 network to enhance its ability to detect small objects. This approach has the advantage of improved accuracy but may increase runtime. To mitigate this problem, the improved YOLOv7 network undergoes model pruning to compress the model size and increase its speed. Experimental results show that the improved YOLOv7 network in this paper improves the average accuracy by 2.9% while maintaining almost the same speed as the original network. After testing, the model has a real-time effect and practical significance. In conclusion, the text-based self-annotated dataset and the improved YOLOv7 network proposed in this paper have important reference values for text-based traffic sign recognition in automatic driving assistance systems.