GUI-Based YOLOv8 License Plate Detection System Design

In the realm of license plate detection technology, there is a growing demand for enhanced accuracy and speed in practical applications. To address this, the YOLO algorithm has been introduced, which offers a comprehensive breakthrough in target detection algorithms. The YOLOv8 network model represents the most recent advancement within the YOLO series. It has demonstrated significant enhancements in both speed and accuracy compared to its predecessors. However, the training and prediction strategy of YOLOv8 differs from earlier iterations of YOLO, necessitating modifications to intricate configuration files. This complexity poses challenges for newcomers to YOLO. To address this issue, a GUI widget has been designed and developed specifically for YOLOv8. In this context, a GUI widget has been designed and developed for YOLOv8. This widget aims to support developers in efficiently completing the training inference task of YOLOv8, while also enhancing development efficiency. Additionally, an MSHA (Multi-Head Attention Mechanism) attention mechanism is introduced to the original YOLOv8 network model for training purposes. This mechanism effectively addresses the issue of overfitting in license plate detection and facilitates the testing of the GUI tool.