Intro
The YOLO v4 Chinese Traffic Sign Recognition model is a deep learning real-time object detection system specifically designed for the complex traffic environment in China, optimized based on the YOLO algorithm to meet the unique needs of traffic sign recognition in China. This model significantly enhances the recognition accuracy of traffic signs of various sizes and angles through an efficient feature extraction network and multi-scale prediction mechanisms, combined with attention mechanisms and improved spatial pyramid pooling techniques, maintaining high accuracy even under varying lighting and adverse weather conditions. Testing on the CCTSDB 2021 dataset showed that the model achieved a detection accuracy of 96.62%, a recall rate of 79.73%, an F-1 score of 87.37%, and an mAP of up to 92.77%, while maintaining a high frame rate of approximately 81 frames per second, meeting the real-time requirements of intelligent vehicles. The model is widely applied in fields such as intelligent transportation systems, environmental perception of autonomous vehicles, urban surveillance, and industrial automation, providing technical support for enhancing road safety and traffic efficiency.
Usage
from modelscope import snapshot_download
model_dir = snapshot_download('Genius-Society/yolov4_tt100k')
Maintenance
git clone [email protected]:Genius-Society/yolov4_tt100k
cd yolov4_tt100k
Mirror
https://www.modelscope.cn/models/Genius-Society/yolov4_tt100k