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Construction Vehicle Images Dataset

32622066
Tagconstruction
Taskobject detection
Release YearMade in 2022
LicenseCC BY-NC-ND 4.0
Download1 GB

Introduction #

The Construction Vehicle Images dataset is an exceptionally demanding collection comprising more than 20,000 original images of construction vehicles. These images were captured and sourced from over 600 urban and rural locations, with each image undergoing manual review and verification by computer vision experts at Datacluster Labs. The dataset provided here is a demo version. For the complete dataset, please refer to the instructions on the homepage.

Parameter Details
Dataset size 20,000+
Captured by Over 1000+ crowdsource contributors
Resolution HD and above (1920x1080 and above)
Location Captured in 600+ cities across India
Diversity Various lighting conditions, day and night, varied distances, view points, etc.
Device used Mobile phones, 2020-2022
Usage Construction site object detection, workplace safety monitoring, self-driving systems, etc.
ExpandExpand
Dataset LinkHomepage

Summary #

Construction Vehicle Images | Trucks | Tractor etc. is a dataset for an object detection task. It is used in the automotive and construction industries.

The dataset consists of 326 images with 427 labeled objects belonging to 2 different classes including truck and tractor.

Images in the Construction Vehicle Images dataset have bounding box annotations. There are 7 (2% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. The dataset was released in 2022 by the DataCluster Labs, India.

Dataset Poster

Explore #

Construction Vehicle Images dataset has 326 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 Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
OpenSample annotation mask from Construction Vehicle ImagesSample image from Construction Vehicle Images
πŸ‘€
Have a look at 326 images
View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 2 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 1-2 of 2
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
truckβž”
rectangle
218
308
1.41
20.61%
tractorβž”
rectangle
107
119
1.11
18.9%

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 1-2 of 2
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
truck
rectangle
308
14.83%
99.84%
0.01%
29px
0.72%
4377px
100%
991px
26.98%
31px
1.19%
3115px
100%
tractor
rectangle
119
17.07%
100%
0.04%
62px
1.9%
3346px
100%
996px
27.15%
54px
2.21%
3120px
100%

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 427 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 427
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”
tractor
rectangle
20210531_18_03_37_000_5tG6wKGaGyPambw1Bz1be8iYudL2_F_4016_3008.jpg
4016 x 3008
3300px
82.17%
3008px
100%
82.17%
2βž”
tractor
rectangle
20210531_11_44_33_000_dTC15H4esgUYVdmtXDxmQcIVIr92_F_3264_2448.jpg
3264 x 2448
1864px
57.11%
2039px
83.29%
47.57%
3βž”
truck
rectangle
Datacluster Truck (193).jpg
3264 x 2448
76px
2.33%
59px
2.41%
0.06%
4βž”
truck
rectangle
Datacluster Truck (193).jpg
3264 x 2448
604px
18.5%
542px
22.14%
4.1%
5βž”
tractor
rectangle
20210602_06_31_02_000_9WQPkiDGniaikLLMKfq2N6pTzGS2_F_4000_3000.jpg
4000 x 3000
933px
23.32%
1482px
49.4%
11.52%
6βž”
truck
rectangle
Datacluster Truck (6).jpg
4000 x 3000
1253px
31.32%
1571px
52.37%
16.4%
7βž”
truck
rectangle
Datacluster Truck (6).jpg
4000 x 3000
214px
5.35%
219px
7.3%
0.39%
8βž”
tractor
rectangle
20210601_14_36_21_000_Db2I2d17GLf53JzlQRSP28hD4Ml1_F_2592_1944.jpg
2592 x 1944
911px
35.15%
946px
48.66%
17.1%
9βž”
tractor
rectangle
20210530_06_33_50_000_kSBbVSEl9VV0elUslY8iz5Ua7N03_F_3264_2448.jpg
3264 x 2448
774px
23.71%
1512px
61.76%
14.65%
10βž”
tractor
rectangle
20210530_10_34_51_000_t8uXBK258XPJg7Y8z44KA58v98u1_F_3000_4000.jpg
4000 x 3000
659px
16.48%
583px
19.43%
3.2%

License #

Construction Vehicle Images | Trucks | Tractor etc. is under CC BY-NC-ND 4.0 license.

Source

Citation #

If you make use of the Construction Vehicle Images data, please cite the following reference:

@dataset{Construction Vehicle Images,
	author={DataCluster Labs},
	title={Construction Vehicle Images | Trucks | Tractor etc.},
	year={2022},
	url={https://www.kaggle.com/datasets/dataclusterlabs/construction-vehicle-images}
}

Source

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

@misc{ visualization-tools-for-construction-vehicle-images-dataset,
  title = { Visualization Tools for Construction Vehicle Images Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/construction-vehicle-images } },
  url = { https://datasetninja.com/construction-vehicle-images },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { mar },
  note = { visited on 2024-03-05 },
}

Download #

Dataset Construction Vehicle Images 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='Construction Vehicle Images', 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.

. . .

Disclaimer #

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