Biformer and NAM attention integrated YOLOv5-OBB model for innovative research in UAV vehicle detection in images

In recent years, with the improvement of living standards and economic development, urban car ownership has increased year by year. This has put pressure on the carrying capacity of roads, leading to traffic congestion. However, for the traditional means of vehicle detection, there are problems such as small detection range, poor flexibility and low efficiency, so that it is not possible to obtain real-time and convenient traffic flow data. In this paper, YOLOv5-OBB is used as a research object, and three experiments are conducted using the DroneVehicle vehicle dataset with the addition of the BiFormer mechanism to strengthen the model's ability to recognise small targets and NAM Attention to improve the model's convergence speed. We observed that the HBBmAP of YOLOv5-OBB increased to 74.1% and [email protected]:0.95 increased to 49.7% after adding the BiFormer mechanism, while the model stability after adding the NAM Attention mechanism was outstanding. This study provides a practical reference for the development of intelligent transport.