In road traffic management, high-speed vehicle detection is often affected by factors such as vehicle speed, weather, camera angle, and image resolution, making vehicle detection on express roads very challenging, Therefore, we propose a Taguchi-based You Only Look Once (YOLOv7) model, called T-YOLOv7, for vehicle detection on high-speed roads. The Taguchi method is used to optimize the combination of hyperparameters of YOLOv7. Experimental results show the precision rate, recall rate, and F1-score of the proposed T-YOLOv7 to be 82.2, 86.3, and 84.2%, respectively. Compared with the original YOLOv7, T-YOLOv7 has improved precision, recall, and F1-score by 12.1, 17.9, and 15.0 percentage points, respectively. Compared with YOLOv4, the improvements in precision, recall, and F1-score using T-YOLOv7 are 4.0, 2.4, and 3.3 percentage points, respectively. The experimental results also show that the proposed T-YOLOv7 is effective in adjusting hyperparameters through the Taguchi method and can be applied to real-time vehicle detection in real environments.