This study details a flame and smoke detection system that integrates the latest YOLOv8 algorithm, and compares it with earlier algorithms such as YOLOv7, YOLOv6, and YOLOv5 for performance evaluation. The system can accurately identify flames and smoke in images, video files, real-time video streams, and batch files.
The article provides Python implementation code, training datasets, and a PySide6-based user interface (UI). The system also integrates the user management function of SQLite database, and supports one-click switching between different versions of YOLO model
Our data set contains a total of 4470 images, including 3847 training images, 405 validation images, and 218 test images. This partitioning is designed to ensure that the model can be trained on sufficient data while leaving enough samples to validate and test the model’s performance.
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