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TTPLA Dataset

123451
Tagenergy-and-utilities
Taskinstance segmentation
Release YearMade in 2020
LicenseApache 2.0
Download3 GB

Introduction #

Rabab Abdelfattah, Xiaofeng Wang, Song Wang

The authors of the TTPLA dataset pursue the task of accurate detection and segmentation of transmission towers (TT) and power lines (PL) in Aerial images. This dataset addresses such challenges as their elongated and thin shapes, colour similarities to the background, and variations in object shape and scene characteristics. The TTPLA dataset comprises 1,100 aerial images, each with a resolution of 3,840 × 2,160 pixels. Additionally, the dataset offers manual annotations for 8,987 instances of TTs and PLs. Unlike other datasets, TTPLA supports not only detection and semantic segmentation but also instance segmentation, providing a comprehensive evaluation platform for computer vision models.

The dataset’s aerial images were extracted from UAV-taken videos and encompass various recording characteristics. To ensure comprehensive coverage, different view angles were captured, including front view, top view, and side view of TTs. This diverse approach aids in training deep learning models to recognize TTs from any perspective. The dataset also considers variations in zooming levels during image capture to accurately represent PLs, particularly in the presence of noisy backgrounds. Various time of day and weather conditions were considered when recording videos, contributing to the dataset’s realism. Backgrounds were given special attention to accurately capture PLs, as the UAV’s viewpoint often results in noisy backgrounds. The dataset reflects this reality by containing PL images with challenging backgrounds, resembling real-world conditions where PLs may exhibit “thin and long” features and blend into the surroundings.

For accurate instance-level labeling of TTs and PLs, the authors employed LabelME. Expert annotators meticulously outlined each instance with polygons. The annotations were consistent across annotators due to the dataset’s top-view orientation, minimal occlusions, clear labeling policies, and the high scrutiny applied by the three expert annotators. The authors provided annotated samples within the TTPLA dataset to showcase the quality of annotations and the dataset’s utility.

Overall, the authors’ TT/PL Aerial-image (TTPLA) dataset presents a valuable resource for advancing the field of object detection and segmentation, especially in the context of transmission towers and power lines within aerial images.

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Dataset LinkHomepageDataset LinkResearch Paper

Summary #

TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines is a dataset for instance segmentation, semantic segmentation, and object detection tasks. It is used in the energy industry.

The dataset consists of 1234 images with 11447 labeled objects belonging to 5 different classes including cable, tower_lattice, tower_wooden, and other: tower_tucohy and void.

Images in the TTPLA dataset have pixel-level instance segmentation annotations. Due to the nature of the instance segmentation task, it can be automatically transformed into a semantic segmentation (only one mask for every class) or object detection (bounding boxes for every object) tasks. All images are labeled (i.e. with annotations). There are 3 splits in the dataset: train (905 images), test (220 images), and val (109 images). The dataset was released in 2020 by the University of South Carolina.

Here is the visualized example grid with animated annotations:

Explore #

TTPLA dataset has 1234 images. Click on one of the examples below or open "Explore" tool anytime you need to view dataset images with annotations. This tool has extended visualization capabilities like zoom, translation, objects table, custom filters and more. Hover the mouse over the images to hide or show annotations.

OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
OpenSample annotation mask from TTPLASample image from TTPLA
👀
Have a look at 1234 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 5 annotation classes in the dataset. Find the general statistics and balances for every class in the table below. Click any row to preview images that have labels of the selected class. Sort by column to find the most rare or prevalent classes.

Search
Rows 1-5 of 5
Class
Images
Objects
Count on image
average
Area on image
average
cable
polygon
1223
10082
8.24
2.27%
tower_lattice
polygon
288
404
1.4
10.18%
tower_wooden
polygon
271
333
1.23
3.05%
tower_tucohy
polygon
161
232
1.44
4.75%
void
polygon
113
396
3.5
0.32%

Co-occurrence matrix #

Co-occurrence matrix is an extremely valuable tool that shows you the images for every pair of classes: how many images have objects of both classes at the same time. If you click any cell, you will see those images. We added the tooltip with an explanation for every cell for your convenience, just hover the mouse over a cell to preview the description.

Images #

Explore every single image in the dataset with respect to the number of annotations of each class it has. Click a row to preview selected image. Sort by any column to find anomalies and edge cases. Use horizontal scroll if the table has many columns for a large number of classes in the dataset.

Object distribution #

Interactive heatmap chart for every class with object distribution shows how many images are in the dataset with a certain number of objects of a specific class. Users can click cell and see the list of all corresponding images.

Class sizes #

The table below gives various size properties of objects for every class. Click a row to see the image with annotations of the selected class. Sort columns to find classes with the smallest or largest objects or understand the size differences between classes.

Search
Rows 1-5 of 5
Class
Object count
Avg area
Max area
Min area
Min height
Min height
Max height
Max height
Avg height
Avg height
Min width
Min width
Max width
Max width
cable
polygon
10082
0.26%
9.88%
0%
4px
0.19%
2160px
100%
766px
35.45%
4px
0.1%
3840px
100%
tower_lattice
polygon
404
7.22%
58.28%
0.04%
59px
2.73%
2160px
100%
1226px
56.77%
34px
0.89%
3840px
100%
void
polygon
396
0.08%
0.92%
0%
6px
0.28%
2160px
100%
453px
20.98%
5px
0.13%
3839px
99.97%
tower_wooden
polygon
333
2.46%
43.61%
0.01%
112px
5.19%
2160px
100%
1061px
49.12%
25px
0.65%
3684px
95.94%
tower_tucohy
polygon
232
3.25%
23.68%
0.02%
137px
6.34%
2160px
100%
1322px
61.22%
29px
0.76%
3385px
88.15%

Spatial Heatmap #

The heatmaps below give the spatial distributions of all objects for every class. These visualizations provide insights into the most probable and rare object locations on the image. It helps analyze objects' placements in a dataset.

Spatial Heatmap

Objects #

Table contains all 11447 objects. Click a row to preview an image with annotations, and use search or pagination to navigate. Sort columns to find outliers in the dataset.

Search
Rows 1-10 of 11447
Object ID
Class
Image name
click row to open
Image size
height x width
Height
Height
Width
Width
Area
1
cable
polygon
45_01811.jpg
2160 x 3840
2160px
100%
1905px
49.61%
0.82%
2
cable
polygon
45_01811.jpg
2160 x 3840
2160px
100%
1956px
50.94%
1.77%
3
cable
polygon
45_01811.jpg
2160 x 3840
2159px
99.95%
1927px
50.18%
0.55%
4
cable
polygon
45_01811.jpg
2160 x 3840
2160px
100%
1942px
50.57%
0.61%
5
cable
polygon
45_01811.jpg
2160 x 3840
1993px
92.27%
1871px
48.72%
0.8%
6
cable
polygon
45_01811.jpg
2160 x 3840
997px
46.16%
980px
25.52%
0.37%
7
cable
polygon
63_00296.jpg
2160 x 3840
2160px
100%
18px
0.47%
0.26%
8
cable
polygon
63_00296.jpg
2160 x 3840
2160px
100%
32px
0.83%
0.3%
9
cable
polygon
63_00296.jpg
2160 x 3840
2160px
100%
46px
1.2%
0.28%
10
cable
polygon
63_00296.jpg
2160 x 3840
956px
44.26%
310px
8.07%
0.23%

License #

TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines is under Apache 2.0 license.

Source

Citation #

If you make use of the TTPLA data, please cite the following reference:

@inproceedings{abdelfattah2020ttpla,
    title={TTPLA: An Aerial-Image Dataset for Detection and Segmentation of Transmission Towers and Power Lines},
    author={Abdelfattah, Rabab and Wang, Xiaofeng and Wang, Song},
    booktitle={Proceedings of the Asian Conference on Computer Vision},
    year={2020}
}

Source

If you are happy with Dataset Ninja and use provided visualizations and tools in your work, please cite us:

@misc{ visualization-tools-for-ttpla-dataset,
  title = { Visualization Tools for TTPLA Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/ttpla } },
  url = { https://datasetninja.com/ttpla },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jun },
  note = { visited on 2024-06-25 },
}

Download #

Dataset TTPLA can be downloaded in Supervisely format:

As an alternative, it can be downloaded with dataset-tools package:

pip install --upgrade dataset-tools

… using following python code:

import dataset_tools as dtools

dtools.download(dataset='TTPLA', dst_dir='~/dataset-ninja/')

Make sure not to overlook the python code example available on the Supervisely Developer Portal. It will give you a clear idea of how to effortlessly work with the downloaded dataset.

The data in original format can be downloaded here.

. . .

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