Objects in aerial images have greater variations in scale and orientation
than in typical images, so detection is more difficult. Convolutional neural
networks use a variety of frequency- and orientation-specific kernels to
identify objects subject to different transformations; these require many
parameters. Sampling equivariant networks can adjust sampling from input
feature maps according to the transformation of the object, allowing a kernel
to extract features of an object under different transformations. Doing so
requires fewer parameters, and makes the network more suitable for representing
deformable objects, like those in aerial images. However, methods like
deformable convolutional networks can only provide sampling equivariance under
certain circumstances, because of the locations used for sampling. We propose
sampling equivariant self-attention networks which consider self-attention
restricted to a local image patch as convolution sampling with masks instead of
locations, and design a transformation embedding module to further improve the
equivariant sampling ability. We also use a novel randomized normalization
module to tackle overfitting due to limited aerial image data. We show that our
model (i) provides significantly better sampling equivariance than existing
methods, without additional supervision, (ii) provides improved classification
on ImageNet, and (iii) achieves state-of-the-art results on the DOTA dataset,
without increased computation.

首次提出了采样等变自注意力网络，通过考虑局部图像区域内的自注意力作为具有蒙版的卷积采样来改善样本等变能力。此外，还通过随机规范化模块解决了由于有限空中图像数据而导致的过度拟合问题。该模型在不需要额外监督的情况下，提供了显著更好的采样等变能力，在 ImageNet 上提供了改进的分类，并在 DOTA 数据集上取得了最先进的结果，并没有增加计算量。

采样等变自注意力网络用于航空图像目标检测

Sampling Equivariant Self-attention Networks for Object Detection in  Aerial Images

In the past decade, object detection has achieved significant progress in
natural images but not in aerial images, due to the massive variations in the
scale and orientation of objects caused by the bird's-eye view of aerial
images. More importantly, the lack of large-scale benchmarks has become a major
obstacle to the development of object detection in aerial images (ODAI). In
this paper,we present a large-scale Dataset of Object deTection in Aerial
images (DOTA) and comprehensive baselines for ODAI. The proposed DOTA dataset
contains 1,793,658 object instances of 18 categories of oriented-bounding-box
annotations collected from 11,268 aerial images. Based on this large-scale and
well-annotated dataset, we build baselines covering 10 state-of-the-art
algorithms with over 70 configurations, where the speed and accuracy
performances of each model have been evaluated. Furthermore, we provide a code
library for ODAI and build a website for evaluating different algorithms.
Previous challenges run on DOTA have attracted more than 1300 teams worldwide.
We believe that the expanded large-scale DOTA dataset, the extensive baselines,
the code library and the challenges can facilitate the designs of robust
algorithms and reproducible research on the problem of object detection in
aerial images.

本研究介绍了一个大规模的 DOTA 数据集，包括从 11,268 个航拍图像中收集的 18 个分类的定向边界框注释的 1,793,658 个物体实例，并基于此构建了覆盖 70 多种配置的 10 种最新算法的基线，还提供了 ODAI 的代码库和用于评估不同算法的网站。

航拍图像中的物体检测：大规模基准和挑战

Object Detection in Aerial Images: A Large-Scale Benchmark and  Challenges

Automatic multi-class object detection in remote sensing images in
unconstrained scenarios is of high interest for several applications including
traffic monitoring and disaster management. The huge variation in object scale,
orientation, category, and complex backgrounds, as well as the different camera
sensors pose great challenges for current algorithms. In this work, we propose
a new method consisting of a novel joint image cascade and feature pyramid
network with multi-size convolution kernels to extract multi-scale strong and
weak semantic features. These features are fed into rotation-based region
proposal and region of interest networks to produce object detections. Finally,
rotational non-maximum suppression is applied to remove redundant detections.
During training, we minimize joint horizontal and oriented bounding box loss
functions, as well as a novel loss that enforces oriented boxes to be
rectangular. Our method achieves 68.16% mAP on horizontal and 72.45% mAP on
oriented bounding box detection tasks on the challenging DOTA dataset,
outperforming all published methods by a large margin (+6% and +12% absolute
improvement, respectively). Furthermore, it generalizes to two other datasets,
NWPU VHR-10 and UCAS-AOD, and achieves competitive results with the baselines
even when trained on DOTA. Our method can be deployed in multi-class object
detection applications, regardless of the image and object scales and
orientations, making it a great choice for unconstrained aerial and satellite
imagery.

本文提出了一种新方法，即一个新的联合图像级联和特征金字塔网络，用于在遥感图像中提取多尺度的强和弱语义特征，并进行定向边界框的检测和旋转非极大值抑制，提高了模型的性能。同时，该模型还具有对图像和对象尺度和方向的鲁棒性，可在多类物体检测应用程序中部署。