Unmanned Aerial Vehicles (UAVs), specifically drones equipped with remote sensing object detection technology, have rapidly gained a broad spectrum of applications and emerged as one of the primary research focuses in the field of computer vision. Although UAV remote sensing systems have the ability to detect various objects, small-scale objects can be challenging to detect reliably due to factors such as object size, image degradation, and real-time limitations. To tackle these issues, a real-time object detection algorithm (YOLO-Drone) is proposed and applied to two new UAV platforms as well as a specific light source (silicon-based golden LED). YOLO-Drone presents several novelties: 1) including a new backbone Darknet59; 2) a new complex feature aggregation module MSPP-FPN that incorporated one spatial pyramid pooling and three atrous spatial pyramid pooling modules; 3) and the use of Generalized Intersection over Union (GIoU) as the loss function. To evaluate performance, two benchmark datasets, UAVDT and VisDrone, along with one homemade dataset acquired at night under silicon-based golden LEDs, are utilized. The experimental results show that, in both UAVDT and VisDrone, the proposed YOLO-Drone outperforms state-of-the-art (SOTA) object detection methods by improving the mAP of 10.13% and 8.59%, respectively. With regards to UAVDT, the YOLO-Drone exhibits both high real-time inference speed of 53 FPS and a maximum mAP of 34.04%. Notably, YOLO-Drone achieves high performance under the silicon-based golden LEDs, with a mAP of up to 87.71%, surpassing the performance of YOLO series under ordinary light sources. To conclude, the proposed YOLO-Drone is a highly effective solution for object detection in UAV applications, particularly for night detection tasks where silicon-based golden light LED technology exhibits significant superiority.

翻译：无人机（UAV），特别是配备遥感目标检测技术的无人机，已迅速获得广泛应用，并成为计算机视觉领域的主要研究焦点之一。尽管无人机遥感系统具备检测多种目标的能力，但由于目标尺寸、图像退化以及实时性限制等因素，小尺度目标的可靠检测仍具挑战性。为解决这些问题，本文提出了一种实时目标检测算法（YOLO-Drone），并将其应用于两种新型无人机平台以及一种特定光源（硅基金色LED）。YOLO-Drone包含以下创新点：1）采用新主干网络Darknet59；2）提出新型复杂特征聚合模块MSPP-FPN，该模块融合了一个空间金字塔池化和三个空洞空间金字塔池化模块；3）使用广义交并比（GIoU）作为损失函数。为评估性能，本文采用了UAVDT和VisDrone两个基准数据集，以及一个在硅基金色LED照明下夜间采集的自制数据集。实验结果表明，在UAVDT和VisDrone数据集上，所提出的YOLO-Drone分别将mAP提升了10.13%和8.59%，优于当前最先进（SOTA）的目标检测方法。在UAVDT数据集上，YOLO-Drone实现了53 FPS的高实时推理速度，同时达到34.04%的最高mAP。值得注意的是，在硅基金色LED照明条件下，YOLO-Drone取得了高达87.71%的mAP，超越了YOLO系列在普通光源下的性能。总之，所提出的YOLO-Drone是无人机目标检测应用中的高效解决方案，尤其适用于硅基金色LED技术具有显著优势的夜间检测任务。