“Unmanned aerial vehicles” (UAVs) are now being used for a wide range of surveillance applications. Specifically, the detection of on-ground vehicles from UAV images has attracted significant attention due to its potential in applications such as traffic management, parking lot management, and facilitating rescue operations in disaster zones and rugged terrains. This paper presents a survey of deep learning techniques for performing on-ground vehicle detection from aerial imagery captured using UAVs (also known as drones). We review the works in terms of their approach to improve accuracy and reduce computation overhead and their optimization objective. We show the similarities and differences of various techniques and also highlight the future challenges in this area. This survey will benefit researchers in the area of artificial intelligence, traffic surveillance, and applications of UAVs.

现在，“无人机”（UAV）被用于各种监视应用。具体而言，由于其在交通管理，停车场管理以及在灾区和崎terrain地形中的救援行动等应用中的潜力，因此从无人机图像中检测地面车辆已引起了广泛的关注。本文介绍了一种深度学习技术的概述，该技术用于从使用无人机（也称为无人机）捕获的航空影像中执行地面车辆检测。我们从工作方法的角度对工作进行回顾，以提高准确性，减少计算开销和优化目标。我们展示了各种技术的异同，并突出了该领域的未来挑战。这项调查将使人工智能领域的研究人员受益，