Unmanned aerial vehicles (UAVs) are emerging as a powerful tool for various industrial and smart city applications. UAVs coupled with various sensors can perform many cognitive tasks such as object detection, surveillance, traffic management, and urban planning. Deep learning has emerged as a popular technique to speed up the processing of high-dimensional data like images and videos, which has led to several applications in surveillance and autonomous driving. However, the area of aerial object detection has been understudied. This work proposes a deep learning approach for detection of objects in aerial scenes captured by UAVs. Our work first categorizes the current methods for aerial object detection using deep learning techniques and discusses how the task is different from general object detection scenarios. We delineate the specific challenges involved and experimentally demonstrate the key design decisions that significantly affect the accuracy and robustness of models. We further propose an optimized architecture that utilizes these optimal design choices along with the recent Res-NeSt backbone to achieve superior performance in aerial object detection. Lastly, we propose several research directions to inspire further advancement in aerial object detection.

中文翻译：

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无人机中的人工智能目标检测：挑战、设计选择和研究方向


无人机 (UAV) 正在成为各种工业和智慧城市应用的强大工具。无人机与各种传感器相结合可以执行许多认知任务，例如物体检测、监视、交通管理和城市规划。深度学习已成为一种流行的技术，可加速图像和视频等高维数据的处理，从而在监控和自动驾驶方面产生了多种应用。然而，空中物体检测领域的研究还不够。这项工作提出了一种深度学习方法，用于检测无人机捕获的空中场景中的物体。我们的工作首先对使用深度学习技术的当前空中物体检测方法进行分类，并讨论该任务与一般物体检测场景的不同之处。我们描述了所涉及的具体挑战，并通过实验证明了显着影响模型准确性和稳健性的关键设计决策。我们进一步提出了一种优化架构，利用这些最佳设计选择以及最近的 Res-NeSt 主干网络来实现空中物体检测的卓越性能。最后，我们提出了几个研究方向，以激发空中物体检测的进一步发展。