Object detection from Unmanned Aerial Vehicles (UAVs) is of great importance in many aerial vision-based applications. Despite the great success of generic object detection methods, a large performance drop is observed when applied to images captured by UAVs. This is due to large variations in imaging conditions, such as varying altitudes, dynamically changing viewing angles, and different capture times. We demonstrate that domain knowledge is a valuable source of information and thus propose domain-aware object detectors by using freely accessible sensor data. By splitting the model into cross-domain and domain-specific parts, substantial performance improvements are achieved on multiple datasets across multiple models and metrics. In particular, we achieve a new state-of-the-art performance on UAVDT for real-time detectors. Furthermore, we create a new airborne image dataset by annotating 13 713 objects in 2 900 images featuring precise altitude and viewing angle annotations.

翻译：无人驾驶航空飞行器(无人驾驶飞行器)的物体探测在许多空视应用中非常重要。尽管通用物体探测方法取得了巨大成功,但在应用无人驾驶飞行器捕获的图像时,观察到性能显著下降。这是因为成像条件差异很大,如高度不同、视野动态变化和捕捉时间不同。我们证明,域知识是宝贵的信息来源,因此通过使用可自由获取的感应数据,提出了有域觉的物体探测器。通过将模型分为跨域和特定域部分,在多个模型和指标的多个数据集上取得了显著的性能改进。特别是,我们在UAVDDT上实现了新的实时探测器最新状态性能。此外,我们创造了一个新的空载图像数据,在2 900个图像中的13 713个物体作了说明,显示精确高度和角度说明。