  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.
