PyQt5-powered frontend for advanced YOLOv8 vehicle detection in challenging backgrounds

Abstract

Object detection, as a key technology in computer vision, has been widely applied across various fields. However, traditional algorithms often need help with poor generalisation and low accuracy, limiting their performance in complex scenarios. With the advent of deep learning, neural networks leveraging large datasets have demonstrated remarkable improvements in generalisation and accuracy, significantly outperforming traditional methods. This study focuses on improving the YOLOv8 algorithm to address detection challenges in complex environments. The enhanced YOLOv8 model incorporates tailored modifications to its network structure, improving its feature extraction capabilities and detection efficiency. A custom vehicle dataset featuring diverse and challenging backgrounds was pre-processed and utilised for training, resulting in a robust vehicle detection model. The experimental results show that the improved YOLOv8 algorithm achieved a recall from 0.469 to 0.479 and [email protected] from 0.520 to 0.533, demonstrating significant performance gains. PyQt5-based graphical user interface was developed, providing a user-friendly platform for real-time detection and analysis. The interface allows users to input images or videos, view detection results, and adjust parameters dynamically, offering both functionality and convenience. This combination of algorithmic enhancement and intuitive interface design establishes a strong foundation for real-world applications and further advancements in multi-target detection and tracking.