Enhancing Intelligent Road Target Monitoring: A Novel BGS-YOLO Approach Based on the YOLOv8 Algorithm

Road target detection is essential for enhancing vehicle safety, increasing operational efficiency, and optimizing user experience. It also forms a crucial part of autonomous driving and intelligent monitoring systems. However, current technologies face significant limitations in multi-level feature fusion and the accurate identification of key targets in complex data environments. To address these challenges, this paper proposes an innovative algorithmic model called BiFPN GAM SimC2f-YOLO (BGS-YOLO), aimed at improving detection performance. Initially, this paper employs the Bidirectional Feature Pyramid Network (BiFPN) to effectively integrate multi-level features. This integration overcomes the limitations in feature extraction and recognition found in existing target detection algorithms. Following this, this paper introduces the Global Attention Module (GAM), which markedly improves the efficiency and accuracy of extracting key target information in complex data environments. Additionally, this paper innovatively designs the SimAM-C2f (SimC2f) network, further advancing feature expressiveness and fusion efficiency. Experiments on the public COCO dataset demonstrate that the BGS-YOLO model significantly outperforms the existing YOLOv8n model. Notably, it shows a 7.3% increase in mean average precision (mAP) and a 2.4% improvement in accuracy. These results highlight the model's high precision and swift response in detecting road targets in complex traffic scenarios. Consequently, the BGS-YOLO model has the potential to significantly enhance road safety and contribute to a considerable reduction in traffic accident rates.