YOLO-FTG—Vehicle Recognition and Detection System Based on Machine Vision in Complex Environments

Severe environmental pollution and complex weather changes significantly hinder the effectiveness of vehicle traffic flow statistics and traffic monitoring technologies. Therefore, achieving fast and accurate vehicle detection in complex environments has become one of the key tasks in the new era. This paper proposes a vehicle detection method for complex environments based on YOLOv11, named YOLO-FTG. First, the neck network of the YOLOv11 baseline model is improved by adding a P2 detection layer and the corresponding detection head. Second, a Spatial-Frequency Hybrid Convolution (SFHC) module is designed. Third, a Global-Local Adaptive Module (GLAM) is proposed. To verify the effectiveness of the proposed model, experiments were conducted on three self-constructed datasets and the public BDD100K dataset. The experimental results demonstrate that compared with existing methods, the YOLO-FTG model achieves higher accuracy, with mAP50 scores of 75.3%, 98.71%, 51.00%, and 64.59% on the four datasets, respectively. These scores represent improvements of 3.24%, 0.34%, 5.64%, and 3.51% over the baseline model, respectively. While maintaining real-time inference speed, these results indicate the effectiveness and robustness of the proposed model in complex environments.