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Electric Pole Detection Dataset

931926
Tagenergy-and-utilities
Taskobject detection
Release YearMade in 2019
Licenseunknown

Summary #

Dataset LinkHomepage

Electric Pole Detection is a dataset for an object detection task. Possible applications of the dataset could be in the utilities industry.

The dataset consists of 93 images with 117 labeled objects belonging to 1 single class (electric pole).

Images in the Electric Pole Detection dataset have bounding box annotations. There is 1 unlabeled image (i.e. without annotations). There are 2 splits in the dataset: train (84 images) and test (9 images). The dataset was released in 2019.

Dataset Poster

Explore #

Electric Pole Detection dataset has 93 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 Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
OpenSample annotation mask from Electric Pole DetectionSample image from Electric Pole Detection
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Have a look at 93 images
Because of dataset's license preview is limited to 12 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 1 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 1-1 of 1
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
electric poleβž”
rectangle
92
117
1.27
6.89%

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 1-1 of 1
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
electric pole
rectangle
117
5.42%
23.09%
0.19%
54px
5.27%
482px
56.12%
238px
27.22%
27px
3.52%
211px
46.3%

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 117 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 117
Object ID
γ…€
Class
γ…€
Image name
click row to open
Image size
height x width
Height
γ…€
Height
γ…€
Width
γ…€
Width
γ…€
Area
γ…€
1βž”
electric pole
rectangle
83.jpg
1024 x 768
260px
25.39%
112px
14.58%
3.7%
2βž”
electric pole
rectangle
83.jpg
1024 x 768
155px
15.14%
71px
9.24%
1.4%
3βž”
electric pole
rectangle
83.jpg
1024 x 768
107px
10.45%
52px
6.77%
0.71%
4βž”
electric pole
rectangle
63.jpg
1024 x 768
279px
27.25%
93px
12.11%
3.3%
5βž”
electric pole
rectangle
31.jpg
768 x 432
368px
47.92%
62px
14.35%
6.88%
6βž”
electric pole
rectangle
18.jpg
768 x 432
218px
28.39%
80px
18.52%
5.26%
7βž”
electric pole
rectangle
18.jpg
768 x 432
111px
14.45%
46px
10.65%
1.54%
8βž”
electric pole
rectangle
42.jpg
768 x 432
285px
37.11%
115px
26.62%
9.88%
9βž”
electric pole
rectangle
28.jpg
768 x 432
270px
35.16%
92px
21.3%
7.49%
10βž”
electric pole
rectangle
30.jpg
768 x 432
270px
35.16%
94px
21.76%
7.65%

License #

License is unknown for the Electric Pole Detection dataset.

Source

Citation #

If you make use of the Electric Pole Detection data, please cite the following reference:

@dataset{Electric Pole Detection,
  author={Bharat Kabra},
  title={Electric Pole Detection},
  year={2019},
  url={https://github.com/kabrabharat/Electric-Pole-detection-using-darknet/tree/master}
}

Source

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

@misc{ visualization-tools-for-electric-pole-dataset,
  title = { Visualization Tools for Electric Pole Detection Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/electric-pole } },
  url = { https://datasetninja.com/electric-pole },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { feb },
  note = { visited on 2024-02-24 },
}

Download #

Please visit dataset homepage to download the data.

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