This paper explores in depth how to apply deep learning technology to develop an advanced common vehicle recognition system. The core of the system adopts the latest YOLOv8 algorithm and compares the performance with earlier YOLOv7, YOLOv6, YOLOv5 and other versions, the corresponding Python code and training data set are provided for easy understanding and application. The system not only supports the recognition of vehicles in static images, but also can process video files, real-time video streams and batch files.
In addition, the study also integrates the user-friendly interface based on PySide6 and the user login registration interface based on SQLite database for management functions, making it easy to operate, while allowing users to easily switch between different YOLO models and customize the interface
The dataset used contained a total of 3,569 images, which were carefully allocated to the training dataset, validation set, and test set, with 2,775, 412, and 382 images, respectively. This allocation is designed to ensure that the model learns enough features during training, adjusts parameters during validation to avoid overfitting, and fairly evaluates model performance during testing.
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
This experiment aims to evaluate and compare the performance of several models YOLOv5, YOLOv6, YOLOv7 and YOLOv8 on the object detection task of vehicle models. To achieve this goal, the blogger trained and tested these four models separately using the same dataset, allowing for a direct performance comparison. The dataset contains images of various vehicle models. This article will compare and analyze the four models, aiming to reveal the advantages and disadvantages of each model and explore the scenario selection of their practical application in industrial environment.
In terms of mAP metrics, YOLOv6n leads with a score of 0.727, showing the best average detection accuracy. This is followed by YOLOv8n with a mAP of 0.693, which is also quite good. YOLOv5nu has a mAP of 0.684, which is slightly lower than YOLOv8n. YOLOv7-tiny has a mAP of 0.623, which is the lowest of the four. This may be due to the fact that the “tiny” version of the model simplifies the network structure in order to reduce the use of computing resources, resulting in lower performance.
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