  Object detection with Unmanned Aerial Vehicles (UAVs) has attracted much
attention in the research field of computer vision. However, not easy to
accurately detect objects with data obtained from UAVs, which capture images
from very high altitudes, making the image dominated by small object sizes,
that difficult to detect. Motivated by that challenge, we aim to improve the
performance of the one-stage detector YOLOv3 by adding a Spatial Pyramid
Pooling (SPP) layer on the end of the backbone darknet-53 to obtain more
efficient feature extraction process in object detection tasks with UAVs. We
also conducted an evaluation study on different versions of YOLOv3 methods.
Includes YOLOv3 with SPP, YOLOv3, and YOLOv3-tiny, which we analyzed with the
VisDrone2019-Det dataset. Here we show that YOLOv3 with SPP can get results mAP
0.6% higher than YOLOv3 and 26.6% than YOLOv3-Tiny at 640x640 input scale and
is even able to maintain accuracy at different input image scales than other
versions of the YOLOv3 method. Those results prove that the addition of SPP
layers to YOLOv3 can be an efficient solution for improving the performance of
the object detection method with data obtained from UAVs.
