This blog post describes the process of building a traffic sign recognition system using deep learning in detail, and provides the complete implementation code. The system uses the advanced YOLOv8 algorithm, and compares with the earlier versions of YOLOv7, YOLOv6, YOLOv5, etc. Performance indicators such as mAP, F1 Score, etc. The article explores the working principle of YOLOv8 algorithm in depth, provides the corresponding Python code, training dataset, and integrates a user-friendly UI interface based on PySide6.
The system can accurately identify traffic signs in a variety of media — such as pictures, picture folders, video files, and real-time video streams. It includes advanced functions such as heat map analysis, marker box categories, and category statistics, and allows adjusting Conf and IOU parameters to optimize recognition effects. The system also designs a user registration and login management interface based on SQLite database, which supports one-click switching between different YOLO models
Our dataset contains 7,444 images, of which 6,516 were used for training, 632 for validation, and 296 for testing. This partitioning ensures that the model can learn on a large enough training dataset, while having the appropriate validation set to adjust the model hyperparameters, and an impartial evaluation of model performance through the test set. The images in the dataset involve a variety of different traffic signs, including but not limited to speed limit, no, warning, and indication categories. These images were taken in a variety of weather conditions, from clear skies to streets after rain to dim light at night, ensuring that the model can maintain high accuracy in a variable real-world environment.
If you have any questions or need the full code or data or need customized development requirements, you can contact me Email Mail me : slowlon@foxmail.com
We use the most advanced YOLOv8 algorithm as the core to implement efficient and accurate face detection in daily scenarios. Compared with earlier deep learning models such as CNN and ResNet, YOLOv8 shows better performance, especially in processing time and detection accuracy. This paper not only introduces the working principle of YOLOv8 algorithm in detail, but also compares the effects of different versions of YOLOv7, YOLOv6, YOLOv5 on face detection tasks through experiments, providing readers with a comprehensive performance evaluation report.
We successfully built a user-friendly face detection system interface using the PySide6 library. This innovation makes face detection in daily scenarios more intuitive and convenient, maximization promotes the popularity of YOLOv8 algorithm in practical applications. The system interface is designed to provide users with a concise and clear operation experience, while ensuring enough functionality and flexibility to meet the needs of different users.
In order to enhance the security and personalized service of the system, we specially designed the login management function. This design not only protects the user’s personal information security, but also leaves room for the introduction of more customized functions in the future, showing the extensible nature of the system.
In order to facilitate the reader’s understanding and application, this paper also provides a complete set of data sets and code resource packages. These resources cover the whole process from training to testing, allowing readers to easily reproduce experimental results and conduct further research and development on this basis.
If you have any questions or need the full code or data or need customized development requirements, you can contact me Email Mail me : slowlon@foxmail.com