准确快速识别空中目标是提高空中交通效率、保障飞行安全的关键。目前，在航空飞行领域，面向飞机目标检测任务的数据集主要为遥感图像所拍摄的俯视视角的地面静止飞机目标，缺少针对空中飞机目标的检测数据集，难以支撑对空中动态飞机目标检测任务的算法理论研究和模型落地应用。为此，本文收集并构建了首个同时标注了正框和斜框的空中飞机目标检测数据集Aerial Aircraft Detection Dataset（AADD），该数据集由公开视频截图整理获得，共包含13类、4121张图像，4409个飞机示例。本文通过训练图像质量分类模型的方式实现了视频中满足质量的截图的自动筛选获取，再人工筛选满足质量要求的图像，最后通过人工标注的方式给出了飞机目标的正框和斜框标注。此外，本文对常见的目标检测算法进行了基准测试，其中YOLOv8检测算法效果最佳，mAP50-95达80.5%，表明本数据集检测任务具有较大挑战性，同时测试结果可作为性能基准支撑相关学者开展研究。

Accurate and rapid recognition of aerial targets is crucial for improving air traffic efficiency and ensuring flight safety. Currently, in the field of aviation flight data, most datasets for aircraft target detection tasks focus on ground-based stationary aircraft targets captured from a top-down view in remote sensing images. There is a lack of datasets specifically designed for the detection of dynamic aerial aircraft targets, which makes it difficult to support the theoretical research on algorithms and the practical application of models for dynamic aerial aircraft target detection tasks. To address this gap, this study collects and constructs the first Aerial Aircraft Detection Dataset (AADD) that includes annotations of both vertical bounding boxes (VBBs) and rotated bounding boxes (RBBs) for aerial aircraft targets. This dataset is compiled from screenshots of public videos, covering 13 categories, 4121 images, and 4409 aircraft instances. In this study, an image quality classification model is trained to realize the automatic screening and acquisition of high-quality screenshots from videos; subsequently, manual screening is conducted to further select images that meet quality requirements, and finally, manual annotation is used to provide AABB and RBB annotations for aircraft targets. In addition, benchmark testing is performed on common object detection algorithms, among which the YOLOv8 detection algorithm achieves the best performance with an mAP50-95 of 80.5%. This result demonstrates the high challenge of detection tasks on this dataset, while the test outcomes can serve as a performance benchmark to support related research by scholars.