Enhanced YOLOv8 with BiFPN and MHSA for traffic vehicle detection

Abstract

Accurately detecting vehicles in urban traffic scenarios is a complex task, especially when dealing with cluttered backgrounds, diverse object scales, and high vehicle density. In this study, we propose an improved YOLOv8-based model tailored for vehicle detection in such challenging environments. The enhancement lies in the integration of a Bidirectional Feature Pyramid Network (BiFPN), which boosts multi-scale feature fusion, and a Multi-Head Self-Attention (MHSA) module, designed to strengthen the model’s capacity to understand broader spatial context. Together, these components help the model better distinguish between densely arranged vehicles. We conducted in-depth experiments on the Vehicles-COCO dataset, and the results demonstrate that our YOLOv8-BiFPN-MHSA variant outperforms the original YOLOv8 not only in Precision but also in mAP. Our model achieves significantly higher [email protected] and [email protected]:0.95, along with an overall improvement in detection performance. These enhancements highlight the stability, efficiency, and strong potential for real-world traffic monitoring systems.