Aerial object detection, as object detection in aerial images captured from an overhead perspective, has been widely applied in urban management, industrial inspection, and other aspects. However, the performance of existing aerial object detection algorithms is hindered by variations in object scales and orientations attributed to the aerial perspective. This survey presents a comprehensive review of recent advances in aerial object detection. We start with some basic concepts of aerial object detection and then summarize the five imbalance problems of aerial object detection, including scale imbalance, spatial imbalance, objective imbalance, semantic imbalance, and class imbalance. Moreover, we classify and analyze relevant methods and especially introduce the applications of aerial object detection in practical scenarios. Finally, the performance evaluation is presented on two popular aerial object detection datasets VisDrone-DET and DOTA, and we discuss several future directions that could facilitate the development of aerial object detection.

航空目标检测，即从俯视角度拍摄的航空图像中进行目标检测，已广泛应用于城市管理、工业检测等方面。然而，现有空中物体检测算法的性能受到空中视角造成的物体尺度和方向变化的阻碍。这项调查全面回顾了空中物体检测的最新进展。我们从空中物体检测的一些基本概念开始，然后总结了空中物体检测的五个不平衡问题，包括尺度不平衡、空间不平衡、目标不平衡、语义不平衡和类别不平衡。此外，我们对相关方法进行了分类和分析，特别介绍了空中目标检测在实际场景中的应用。最后，对两个流行的空中物体检测数据集 VisDrone-DET 和 DOTA 进行了性能评估，并讨论了可以促进空中物体检测发展的几个未来方向。