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

通过针对小目标检测任务设计的联合损失函数，我们提出了一种名为 JointYODNet 的新颖方法，用于通过无人机图像和雷达数据进行小目标检测。我们的方法在各种环境条件下的无人机图像数据集上进行了广泛实验，并评估了不同变体的损失函数，确定了最有效的配方。结果表明，我们的联合损失函数在准确定位小目标方面优于现有方法，具有 0.971 的召回率和 0.975 的 F1 分数，超过了最先进的技术。此外，我们的方法在不同尺度下检测小目标的鲁棒性表现出了 98.6% 的 mAP@.5 (%)。

Joint-YODNet：一种轻量级的无人机目标检测器以实现超过 100 帧每秒

Joint-YODNet: A Light-weight Object Detector for UAVs to Achieve Above  100fps

3D point cloud completion, the task of inferring the complete geometric shape
from a partial point cloud, has been attracting attention in the community. For
acquiring high-fidelity dense point clouds and avoiding uneven distribution,
blurred details, or structural loss of existing methods' results, we propose a
novel approach to complete the partial point cloud in two stages. Specifically,
in the first stage, the approach predicts a complete but coarse-grained point
cloud with a collection of parametric surface elements. Then, in the second
stage, it merges the coarse-grained prediction with the input point cloud by a
novel sampling algorithm. Our method utilizes a joint loss function to guide
the distribution of the points. Extensive experiments verify the effectiveness
of our method and demonstrate that it outperforms the existing methods in both
the Earth Mover's Distance (EMD) and the Chamfer Distance (CD).

本文提出了一种新的方法，通过两个阶段，利用参数化表面元素和采样算法预测粗粒度点云并与输入点云合并来完成部分点云。实验结果表明，该方法在 Earth Mover's Distance（EMD）和 Chamfer Distance（CD）等方面表现优异，优于现有方法。