Joint-YODNet: A Light-weight Object Detector for UAVs to Achieve Above
  100fps
Vipin Gautam Shitala Prasad Sharad Sinha
Small object detection via UAV (Unmanned Aerial Vehicle) images captured from
drones and radar is a complex task with several formidable challenges. This
domain encompasses numerous complexities that impede the accurate detection and
localization of small objects. To address these challenges, we propose a novel
method called JointYODNet for UAVs to detect small objects, leveraging a joint
loss function specifically designed for this task. Our method revolves around
the development of a joint loss function tailored to enhance the detection
performance of small objects. Through extensive experimentation on a diverse
dataset of UAV images captured under varying environmental conditions, we
evaluated different variations of the loss function and determined the most
effective formulation. The results demonstrate that our proposed joint loss
function outperforms existing methods in accurately localizing small objects.
Specifically, our method achieves a recall of 0.971, and a F1Score of 0.975,
surpassing state-of-the-art techniques. Additionally, our method achieves a
mAP@.5(%) of 98.6, indicating its robustness in detecting small objects across
varying scales