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https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset
https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset
Last synced: 26 days ago
JSON representation
- Host: GitHub
- URL: https://github.com/CrazyStoneonRoad/TGRS-HRRSD-Dataset
- Owner: CrazyStoneonRoad
- License: other
- Created: 2018-10-08T12:42:04.000Z (over 5 years ago)
- Default Branch: mstr-gthb
- Last Pushed: 2021-05-14T02:24:45.000Z (about 3 years ago)
- Last Synced: 2024-02-23T08:40:36.665Z (4 months ago)
- Language: MATLAB
- Size: 8.9 MB
- Stars: 132
- Watchers: 5
- Forks: 42
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-object-detection-in-aerial-images - HRRSD - Resolution Remote Sensing Object Detection</font> | [TGRS](https://ieeexplore.ieee.org/document/8676107) (Dataset)
- awesome-semantic-understanding-for-aerial-scene - **2019TGRS**
README
TGRS-HRRSD-Dataset: *High Resolution Remote Sensing Detection* (HRRSD)
=====================
# I. NOTE: JPEG files are available on [BaiduCloud](https://pan.baidu.com/s/1ainmXaL_Mu5XASk3ydhqKA#list/path=%2F&parentPath=%2F), [GoogleDrive](https://drive.google.com/open?id=1bffECWdpa0jg2Jnm7V0oCyFFh0N-EIkr), and an ipv6 site [bt.byr.cn](https://bt.byr.cn/details.php?id=298274&edited=1)
- HRRSD contains **21,761 images** acquired from Google Earth and Baidu Map with the spatial resolution from 0.15-m to 1.2-m.
- There are **55,740 object instances** in HRRSD.
- HRRSD contains **13 categories** of RSI objects.
Moreover, this dataset is divided as several subsets, image numbers in each subset are **5401 for ‘train’, 5417 for ‘val’, and 10943 for ‘test’**. And ‘train-val’ subset is a merge of ‘train’ and ‘val’.
# II. Mean and Std
In most current object detection systems, means and std values of datasets are required.
You may refer to:
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375]
Moreover, you can compute the values by yourself with file ```OPT2017/avr_std_detection_sets.py```:
```shell
$ cd your_HRRSD_path/OPT2017
$ python avr_std_detection_sets.py 500
```
# III. Folders
## Labels
+ /OPT2017/Annotations: \*.xml
+ /OPT2017/labels: \*.txt *with the form of (class x y width height)*
## Images
+ /OPT2017/JPEGImages: \*.jpg
## Dataset Division
+ /OPT2017/ImageSets/Main: Division of the dataset.
# IV. Statistics and Benchmark
## Statistics
Label|Name|N_Train|N_Val|N_Trainval|N_Test|N_All|Mean Resized Scale /pixel|Resized Scale Std /pixel
:-: |:-: |:-: |:-: |:-: |:-: |:-: |:-: |:-:
1| ship |950|948|1898|1988|3886|167.44|110.37
2| bridge |1123|1121|2244|2326|4570|246.10|110.53
3| ground track field |859|856|1717|2017|3734|276.50|100.65
4| storage tank |1099|1092|2191|2215|4406|125.60|68.41
5| basketball court |923|920|1843|2033|3876|108.19|57.46
6| tennis court |1043|1040|2083|2212|4295|102.71|38.80
7| airplane |1226|1222|2448|2451|4899|113.21|67.98
8| baseball diamond |1007|1004|2011|2022|4033|231.61|117.85
9| harbor |967|964|1931|1953|3884|163.96|94.16
10| vehicle |1188|1186|2374|2382|4756|41.96|9.99
11| crossroad |903|901|1804|2219|4023|220.54|59.24
12| T junction |1066|1065|2131|2289|4420|198.71|54.88
13| parking lot |1241|1237|2478|2480|4958|122.85|54.45
In this table, N_* refers to numbers of objects. 'Train', 'Val', 'Test' are three subsets of the dataset. 'Mean Resized Scale' shows average scale of each category. 'Resized Scale Std' is the standard deviation of category scale.
## Benchmark
Category |YOLO-v2 /%|Fast R-CNN /%|Fast_R-CNN_r50 + GACL-Net /%|Faster R-CNN /%|Faster_R-CNN_r50 + GACL-Net /%
:-: |:-: |:-: |:-: |:-: |:-:
Airplane |84.6|83.3|85.1|90.8|90.8
Baseball Diamond |62.2|83.6|82.6|86.9|87.2
Basketball Court |41.3|36.7|42.1|47.9|49.7
Bridge |79.0|75.1|76.7|85.5|85.6
Crossroad |43.4|67.1|68.7|88.6|88.2
Ground Track Field|94.4|90.0|89.6|90.6|90.7
Harbor |74.4|76.0|78.4|89.4|89.7
Parking Lot |45.8|37.5|39.5|63.3|65.3
Ship |78.5|75.0|74.3|88.5|88.5
Storage Tank |72.4|79.8|80.4|88.7|89.2
T Junction |46.8|39.2|38.8|75.1|75.0
Tennis Court |67.6|75.0|77.0|80.7|80.8
Vehicle |65.1|46.1|50.7|84.0|86.9
Mean AP |65.8|66.5|68.0|81.5|82.1
GACL-Net \cite{lu2020gated} is a method proposed to improve object localization performance. Title of this paper is "Gated and Axis-Concentrated Localization Network for Remote Sensing Object Detection". The suffix "r50" denotes using 50 layer Resnet.
# V. FAQ
If any question is met, please contanct me with the e-mail: [email protected].
Qestion 1: AP for the "T junction" class is always NAN or 0, why?
Anwser Q1: In some object detection frameworks, there may be a piece of code like "cls_names = lower( cls_names )".
This will set class names to lower case, but class names in xml files contain "T junction" where "T" is uppercase.
This actually will cause several problems.
The solution is using debug sofwares to find the code of changing word cases and correct it.
For the dataset, I won't change the "T junction" labels in xmls currently for lacking time.
# VI. Citation
If you find HRRSD dataset useful in your research, please consider citing:
```
@article{zhang2019hierarchical,
title={Hierarchical and Robust Convolutional Neural Network for Very High-Resolution Remote Sensing Object Detection},
author={Zhang, Yuanlin and Yuan, Yuan and Feng, Yachuang and Lu, Xiaoqiang},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={57},
number={8},
pages={5535--5548},
year={2019},
publisher={IEEE}
}
```
For more comparative experimental results, please refer to:
```
@article{lu2020gated,
title={Gated and Axis-Concentrated Localization Network for Remote Sensing Object Detection},
author={Lu, Xiaoqiang and Zhang, Yuanlin and Yuan, Yuan and Feng, Yachuang},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={58},
number={1},
pages={179--192},
year={2020},
publisher={IEEE}
}
```