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Vehicle target detection is a key technology for reconnaissance unmanned aerial vehicles (UAVs). However, in order to obtain a larger reconnaissance field of view, this type of UAV generally flies at a higher altitude, resulting in a relatively small proportion of vehicle targets in its imaging images. Moreover, due to the unique nature of the mission, previously unseen vehicle types are prone to appearing in the surveillance area. Additionally, it is challenging for large-scale detectors based on deep learning to achieve real-time performance on UAV computing equipment. To address these problems, we propose a vehicle object detector specifically designed for UAVs in this paper. We have made modifications to the backbone of Faster R-CNN based on the target and scene characteristics. We have improved the positioning accuracy of small-scale imaging targets by adjusting the size and ratio of anchors. Furthermore, we have introduced a postprocessing method for out-of-distribution detection, enabling the designed detector to detect and distinguish untrained vehicle types. Additionally, to tackle the scarcity of reconnaissance images, we have constructed two datasets using modeling and image rendering techniques. We have evaluated our method on these constructed datasets. The proposed method achieves a 96% mean Average Precision at IoU threshold 0.5 (mAP50) on trained objects and a 71% mAP50 on untrained objects. Equivalent flight experiments demonstrate that our model, trained on synthetic data, can achieve satisfactory detection performance and computational efficiency in practical applications.

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