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<!DOCTYPE html>
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<title>Detecting Objects in Aerial Images (DOAI)</title>
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<h2 style="text-align:center; margin-top:60px; font-weight: bold;">
Challenge-2019 on
</h2>
<h1 style="text-align:center; font-weight: bold; color:#FF9900; padding-top:10px;">
Object Detection in Aerial Images
</h1>
<h2 style="text-align:center; font-weight: bold; font-style: italic">
<span class="subheading" style="text-align:center; font-weight:bold; font-style: italic">
June 16, 2019, Long Beach, California.
</h2>
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<!-- Main Content -->
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<h2>
Overview
</h2>
<p>
We propose three detection tasks. Task1 is to detect instances with oriented bounding boxes. Task2 is to detect instances
with horizontal bounding boxes.
<!-- Task3 is jointly object detection and orientation estimation for movable instances -->
<!-- The results from task2 are of great practical value. -->
<!-- We recommond you to test your algorithms by way of Task1. -->
You can use the provided train/val data to train and validate your detector. Validation data may also be used for training
when submitting results on the test set. External datas of any form is allowed. But must be reported
during submission. Fine-tuning models that are pretrained on ImageNet or COCO are also allowed.
</p>
<h2>
<b>Task1</b> - Detection with oriented bounding boxes
<!--<strong>(Recommended)</strong> -->
</h2>
<p>
The purpose of this task is to localize the ground object instances with an oriented bounding box. The oriented bounding
box follows the same format with the original annotation {(x
<sub>i</sub>, y
<sub>i</sub>), i = 1,2,3,4}.
</p>
<!-- <h3>
Evaluation Server
</h3>
<p>
For evaluation, you must registrate and submit on the
<a href="http://www.icdar2017chinese.site:5080/evaluation1/">Evaluation Server</a>
</p> -->
<h3>
Submission Format
</h3>
<p>
You will be asked to submit a zip file <a href="example_Task1.zip">(example_Task1.zip)</a> containing results for all test images to evaluate your results.
The results are stored in 16 files, <strong style="color:blue">"Task1_plane.txt, Task1_storage-tank.txt, ..."</strong>, each file contains all the results for a specific category.
Each file is in the following format:
</p>
<div class="alert alert-secondary" role="alert" style="font-size:18px;font-style: italic;font-family:'Times New Roman', Times, serif">
imgname score x<sub>1</sub> y<sub>1</sub> x<sub>2</sub> y<sub>2</sub> x<sub>3</sub> y<sub>3</sub> x<sub>4</sub> y<sub>4</sub> <br>
imgname score x<sub>1</sub> y<sub>1</sub> x<sub>2</sub> y<sub>2</sub> x<sub>3</sub> y<sub>3</sub> x<sub>4</sub> y<sub>4</sub> <br>
...
<!-- x
<sub>1</sub> y
<sub>1</sub> x
<sub>2</sub> y
<sub>2</sub> x
<sub>3</sub> y
<sub>3</sub> x
<sub>4</sub> y
<sub>4</sub> category score
<br> x
<sub>1</sub> y
<sub>1</sub> x
<sub>2</sub> y
<sub>2</sub> x
<sub>3</sub> y
<sub>3</sub> x
<sub>4</sub> y
<sub>4</sub> category score
<br> ... -->
</div>
<h3>
Evaluation Protocol
</h3>
<p>
The evaluation protocol for oriented bounding box is a little different from the protocol in the original PASCAL VOC. We
use the intersection over the union area of two polygons(ground truth and prediction) to calculate the
IoU. The rest follows the PASCAL VOC.
</p>
<h2>
<b>Task2</b> - Detection with horizontal bounding boxes
</h2>
<p>
Detecting object with horizontal bounding boxes is usual in many previous contests for object detection. The aim of this
task is to accurately localize the instance in terms of horizontal bounding box with (x, y, w, h) format.
In the task, the ground truths for training and testing are generated by calculating the axis-aligned
bounding boxes over original annotated bounding boxes.
</p>
<h3>
Submission Format
</h3>
<p>
You will be asked to submit a zip file <a href="example_Task2.zip">(example_Task2.zip)</a> containing results for all test images to evaluate your results.
The results are stored in 16 files,
<strong style="color:blue">"Task2_plane.txt, Task2_storage-tank.txt, ..."</strong>, each file contains all the results for a specific category.
The format of the results is:
</p>
<div class="alert alert-secondary" role="alert" style="font-size:18px;font-style: italic;font-family:'Times New Roman', Times, serif">
<!-- x y w h category score
<br> x y w h category score
<br> ... -->
imgname score xmin ymin xmax ymax <br>
imgname score xmin ymin xmax ymax <br>
...
</div>
<!-- <a href="submissionformat/example_task2.rar">An example submission of task1</a> -->
<h3>
Evaluation Protocol
</h3>
<p>
The evaluation protocol for horizontal bounding boxes follows the PASCAL VOC benchmark, which uses mean Average Precision(
<strong>mAP</strong>) as the primary metric.
</p>
<!-- <h2>
<b>Task3</b> - Jointly object detection and orientation estimation for movable instances
</h2>
<p>
This task aims to estimate the orientation for movable instances(vehicles, planes, and ships), which is important when applied
to tracking. To make it clear, in this task, each instance's location and orientation is represented
by (x, y, w, h, θ) transferred from {(x
<sub>i</sub>, y
<sub>i</sub>), i = 1,2,3,4}.
</p> -->
<!-- <h3>
Evaluation Server
</h3>
<p>
For evaluation, you must registrate and submit on the
<a href="http://www.icdar2017chinese.site:5080/evaluation1/">Evaluation Server</a>
</p> -->
<!-- <h3>
Submission Format
</h3>
<p>
You will be asked to submit a zip file containing results for all test images to evaluate your results.
The format of the results is:
</p>
<div class="alert alert-secondary" role="alert" style="font-size:18px;font-style: italic;font-family:'Times New Roman', Times, serif">
x y w h Θ category score
<br> x y w h Θ category score
<br> ...
</div>
<h3>
Evaluation Protocol
</h3>
<p>
Note that the evaluation protocol of this task is slightly different from that of Task 2. In Task 2, if the IoU between the
predicted box and ground truth is more than a certain threshold, it is assigned to be true positive(TP).
While in Task3, in addition to requiring IoU to be more than a threshold, the difference in angle is
required to be less than a certain threshold. As a result, the mAP for moveable classes in Task2 is upbound
of Task3. There is a similar metric in PASCAL3D+ which was called Average Viewpoint Precision(
<strong>AVP</strong>).
</p> -->
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