Automated Aerial Triangulation (AAT), aiming to restore image pose and
reconstruct sparse points simultaneously, plays a pivotal role in earth
observation. With its rich research heritage spanning several decades in
photogrammetry, AAT has evolved into a fundamental process widely applied in
large-scale Unmanned Aerial Vehicle (UAV) based mapping. Despite its
advancements, classic AAT methods still face challenges like low efficiency and
limited robustness. This paper introduces DeepAAT, a deep learning network
designed specifically for AAT of UAV imagery. DeepAAT considers both spatial
and spectral characteristics of imagery, enhancing its capability to resolve
erroneous matching pairs and accurately predict image poses. DeepAAT marks a
significant leap in AAT's efficiency, ensuring thorough scene coverage and
precision. Its processing speed outpaces incremental AAT methods by hundreds of
times and global AAT methods by tens of times while maintaining a comparable
level of reconstruction accuracy. Additionally, DeepAAT's scene clustering and
merging strategy facilitate rapid localization and pose determination for
large-scale UAV images, even under constrained computing resources. The
experimental results demonstrate DeepAAT's substantial improvements over
conventional AAT methods, highlighting its potential in the efficiency and
accuracy of UAV-based 3D reconstruction tasks. To benefit the photogrammetry
society, the code of DeepAAT will be released at:
this https URL

DeepAAT 是一种专为无人机图像的自动航空三角测量设计的深度学习网络，通过考虑图像的空间和光谱特征来提高精准地解决错误匹配对和预测图像姿态的能力，从而大幅提高了自动航空三角测量的效率和精确度。

DeepAAT: 快速无人机遥感制图的深度自动空三

DeepAAT: Deep Automated Aerial Triangulation for Fast UAV-based Mapping

In forest industry, mechanical site preparation by mounding is widely used
prior to planting operations. One of the main problems when planning planting
operations is the difficulty in estimating the number of mounds present on a
planting block, as their number may greatly vary depending on site
characteristics. This estimation is often carried out through field surveys by
several forestry workers. However, this procedure is prone to error and
slowness. Motivated by recent advances in UAV imagery and artificial
intelligence, we propose a fully automated framework to estimate the number of
mounds on a planting block. Using computer vision and machine learning, we
formulate the counting task as a supervised learning problem using two
prediction models. A local detection model is firstly used to detect visible
mounds based on deep features, while a global prediction function is
subsequently applied to provide a final estimation based on block-level
features. To evaluate the proposed method, we constructed a challenging UAV
dataset representing several plantation blocks with different characteristics.
The performed experiments demonstrated the robustness of the proposed method,
which outperforms manual methods in precision, while significantly reducing
time and cost.

通过使用计算机视觉和机器学习，提出了一个全自动框架来估算种植区的土丘数量，该方法在精确度上优于手动方法，同时显著降低了时间和成本。

种植微环境的自动计数：基于局部图像识别与全局计数估计

Automatic counting of planting microsites via local visual detection and  global count estimation

On-board real-time vehicle detection is of great significance for UAVs and
other embedded mobile platforms. We propose a computationally inexpensive
detection network for vehicle detection in UAV imagery which we call
ShuffleDet. In order to enhance the speed-wise performance, we construct our
method primarily using channel shuffling and grouped convolutions. We apply
inception modules and deformable modules to consider the size and geometric
shape of the vehicles. ShuffleDet is evaluated on CARPK and PUCPR+ datasets and
compared against the state-of-the-art real-time object detection networks.
ShuffleDet achieves 3.8 GFLOPs while it provides competitive performance on
test sets of both datasets. We show that our algorithm achieves real-time
performance by running at the speed of 14 frames per second on NVIDIA Jetson
TX2 showing high potential for this method for real-time processing in UAVs.

本文提出了一种名为 ShuffleDet 的计算效率高、适用于 UAV 图像的车辆检测网络，通过使用通道重组和分组卷积等方法实现了提高速度性能的目的。该算法在 CARPK 和 PUCPR + 数据集上的表现也显示了其具有竞争性的性能，能够达到 14fps 的实时处理速度，展现其在 UAV 实时处理领域的高潜力。