Small oriented objects that represent tiny pixel-area in large-scale aerial
images are difficult to detect due to their size and orientation. Existing
oriented aerial detectors have shown promising results but are mainly focused
on orientation modeling with less regard to the size of the objects. In this
work, we proposed a method to accurately detect small oriented objects in
aerial images by enhancing the classification and regression tasks of the
oriented object detection model. We designed the Attention-Points Network
consisting of two losses: Guided-Attention Loss (GALoss) and Box-Points Loss
(BPLoss). GALoss uses an instance segmentation mask as ground-truth to learn
the attention features needed to improve the detection of small objects. These
attention features are then used to predict box points for BPLoss, which
determines the points' position relative to the target oriented bounding box.
Experimental results show the effectiveness of our Attention-Points Network on
a standard oriented aerial dataset with small object instances (DOTA-v1.5) and
on a maritime-related dataset (HRSC2016). The code is publicly available.

我们提出了一种方法，通过增强定向目标检测模型的分类和回归任务，准确地检测空中图像中的小型定向目标。我们设计了一个 Attention-Points 网络，其中包含两个损失函数：Guided-Attention Loss（GALoss）和 Box-Points Loss（BPLoss）。实验结果表明，我们的 Attention-Points 网络在标准的小型目标空中数据集（DOTA-v1.5）和海事相关数据集（HRSC2016）中具有很好的效果。代码公开可用。

提升航空图片中小型定向物体的检测

Improving the Detection of Small Oriented Objects in Aerial Images

Object detection has achieved remarkable progress in the past decade.
However, the detection of oriented and densely packed objects remains
challenging because of following inherent reasons: (1) receptive fields of
neurons are all axis-aligned and of the same shape, whereas objects are usually
of diverse shapes and align along various directions; (2) detection models are
typically trained with generic knowledge and may not generalize well to handle
specific objects at test time; (3) the limited dataset hinders the development
on this task. To resolve the first two issues, we present a dynamic refinement
network that consists of two novel components, i.e., a feature selection module
(FSM) and a dynamic refinement head (DRH). Our FSM enables neurons to adjust
receptive fields in accordance with the shapes and orientations of target
objects, whereas the DRH empowers our model to refine the prediction
dynamically in an object-aware manner. To address the limited availability of
related benchmarks, we collect an extensive and fully annotated dataset,
namely, SKU110K-R, which is relabeled with oriented bounding boxes based on
SKU110K. We perform quantitative evaluations on several publicly available
benchmarks including DOTA, HRSC2016, SKU110K, and our own SKU110K-R dataset.
Experimental results show that our method achieves consistent and substantial
gains compared with baseline approaches. The code and dataset are available at
this https URL

本研究提出了一种动态细化网络解决因神经元的感受野方向固定及模型泛化性差而导致的面对有方向的、密集的对象的检测困难问题，并基于发起的 SKU110K 数据集提出了一种新的定向边界框注释方式，实验结果显示该方法与基线方法相比具有相当大的优势。

面向方向不定且密集排列的物体检测的动态细化网络

Dynamic Refinement Network for Oriented and Densely Packed Object  Detection

The detection of oriented objects is frequently appeared in the field of
natural scene text detection as well as object detection in aerial images.
Traditional detectors for oriented objects are common to rotate anchors on the
basis of the RCNN frameworks, which will multiple the number of anchors with a
variety of angles, coupled with rotating NMS algorithm, the computational
complexities of these models are greatly increased. In this paper, we propose a
novel model named Oriented Objects Detection Network O^2-DNet to detect
oriented objects by predicting a pair of middle lines inside each target.
O^2-DNet is an one-stage, anchor-free and NMS-free model. The target line
segments of our model are defined as two corresponding middle lines of original
rotating bounding box annotations which can be transformed directly instead of
additional manual tagging. Experiments show that our O^2-DNet achieves
excellent performance on ICDAR 2015 and DOTA datasets. It is noteworthy that
the objects in COCO can be regard as a special form of oriented objects with an
angle of 90 degrees. O^2-DNet can still achieve competitive results in these
general natural object detection datasets.

本文提出了一种名为 O^2-DNet 的新型模型，该模型可以通过预测每个目标内的一对中间直线来检测有方向的对象，并且它是一种一级、无锚点和无 NMS 的模型。

以中线对的形式表现定向物体

Oriented Objects as pairs of Middle Lines

We propose a novel method for representing oriented objects in aerial images
named Adaptive Period Embedding (APE). While traditional object detection
methods represent object with horizontal bounding boxes, the objects in aerial
images are oritented. Calculating the angle of object is an yet challenging
task. While almost all previous object detectors for aerial images directly
regress the angle of objects, they use complex rules to calculate the angle,
and their performance is limited by the rule design. In contrast, our method is
based on the angular periodicity of oriented objects. The angle is represented
by two two-dimensional periodic vectors whose periods are different, the vector
is continuous as shape changes. The label generation rule is more simple and
reasonable compared with previous methods. The proposed method is general and
can be applied to other oriented detector. Besides, we propose a novel IoU
calculation method for long objects named length independent IoU (LIIoU). We
intercept part of the long side of the target box to get the maximum IoU
between the proposed box and the intercepted target box. Thereby, some long
boxes will have corresponding positive samples. Our method reaches the 1st
place of DOAI2019 competition task1 (oriented object) held in workshop on
Detecting Objects in Aerial Images in conjunction with IEEE CVPR 2019.

提出了一种名为自适应周期嵌入（APE）的用于表示航空图像中有向物体的新方法，它基于有向物体的角度周期性来表示角度，并使用更简单，更合理的标签生成规则和长边相交 IoU（LIIoU）新方法。