ABSTRACT Unmanned Aerial Vehicles (UAVs) are promising technologies within many different application scenarios including human detection in search and rescue and surveillance use cases, which have received considerable attention worldwide. However, adverse conditions, such as varying altitude, overhead camera placement, changing illumination and moving platform, impose challenges for high-performance yet cost-efficient human detection. To overcome these challenges, we propose a novel combination of dilated convolutions with Path Aggregation Network (PAN) as a new deep neural network-based human detection algorithm in real time. Furthermore, we establish a comprehensive human detection dataset with varying backgrounds, illuminations, and contrast and train the proposed machine-learning model on the collected dataset. Our approach achieves both high precision (88.0% mean Average Precision (mAP)) and real time (67.0 Frames Per Second (FPS)) on a commercial off-the-shelf PC platform. In terms of accuracy, the result is comparable to the standard You Only Look Once v3 (YOLOv3). However, the speed is twice as that of the standard YOLOv3. YOLOv4 is slightly more accurate (89.8%) than our approach. However, it is slower (38.0 versus 67.0 FPS) and has more Billion Floating-Point Operations (BFLOPS). The proposed algorithm has also trained with the VisDrone2019 dataset and compared with seven studies using this dataset. The results have further validated the effectiveness of the proposed approach. Moreover, the algorithm has been evaluated on an embedded system (Jetson AGX Xavier), which demonstrates the usefulness of this method on power-constrained devices. The proposed algorithm is fast, memory efficient, and computationally less expensive to achieve high detection performance. It is expected to contribute significantly to the wider use of UAV applications including search and rescue missions to locate missing people, and surveillance particularly for applications running on resource-constrained platforms, like smartphones or tablets. This proposed system is now being used in aerial drone system of Police of Scotland to help them locate and find missing and vulnerable people. The results of the project were broadcasted by BBC Scotland.

中文翻译：

---

使用无人机光学图像进行高效的实时人体检测

摘要 无人驾驶飞行器 (UAV) 是许多不同应用场景中的有前途的技术，包括搜救和监视用例中的人体检测，在全球范围内受到了相当大的关注。然而，不利条件，例如不同的高度、高架摄像机的放置、不断变化的照明和移动平台，对高性能且具有成本效益的人体检测提出了挑战。为了克服这些挑战，我们提出了一种新的扩张卷积与路径聚合网络 (PAN) 的组合，作为一种新的基于深度神经网络的实时人体检测算法。此外，我们建立了一个具有不同背景、照明和对比度的综合人体检测数据集，并在收集的数据集上训练所提出的机器学习模型。我们的方法在商用现成 PC 平台上实现了高精度（88.0% 平均精度 (mAP)）和实时（67.0 帧每秒 (FPS)）。在准确性方面，结果与标准 You Only Look Once v3 (YOLOv3) 相当。但是，速度是标准YOLOv3的两倍。YOLOv4 比我们的方法更准确（89.8%）。但是，它更慢（38.0 与 67.0 FPS）并且具有更多的十亿浮点运算 (BFLOPS)。所提出的算法还使用 VisDrone2019 数据集进行了训练，并与使用该数据集的七项研究进行了比较。结果进一步验证了所提出方法的有效性。此外，该算法已经在嵌入式系统 (Jetson AGX Xavier) 上进行了评估，这证明了该方法在功率受限设备上的实用性。所提出的算法速度快，内存效率高，并且计算成本较低，可以实现高检测性能。预计它将为无人机应用的更广泛使用做出重大贡献，包括搜索和救援任务以定位失踪人员，以及监视特别是对在资源受限平台（如智能手机或平板电脑）上运行的应用程序。这个提议的系统现在正在苏格兰警察的空中无人机系统中使用，以帮助他们定位和寻找失踪和易受伤害的人。该项目的结果由 BBC 苏格兰广播公司播出。和监视，尤其是在资源受限平台（如智能手机或平板电脑）上运行的应用程序。这个提议的系统现在正在苏格兰警察的空中无人机系统中使用，以帮助他们定位和寻找失踪和易受伤害的人。该项目的结果由 BBC 苏格兰广播公司播出。和监视，尤其是在资源受限平台（如智能手机或平板电脑）上运行的应用程序。这个提议的系统现在正在苏格兰警察的空中无人机系统中使用，以帮助他们定位和寻找失踪和易受伤害的人。该项目的结果由 BBC 苏格兰广播公司播出。