Construction Vehicle Images

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.

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Homepage

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.

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.

Sample annotation mask from Construction Vehicle Images

Sample image from Construction Vehicle Images

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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.

Rows 1-2 of 2

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
truck➔ rectangle	218	308	1.41	20.61%
tractor➔ 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.

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.

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.

Rows 1-10 of 427

Object ID ㅤ	Class ㅤ	Image name click row to open	Image size height x width	Height ㅤ	Height ㅤ	Width ㅤ	Width ㅤ	Area ㅤ
1➔	tractor rectangle	20210531_18_03_37_000_5tG6wKGaGyPambw1Bz1be8iYudL2_F_4016_3008.jpg	4016 x 3008	3300px	82.17%	3008px	100%	82.17%
2➔	tractor rectangle	20210531_11_44_33_000_dTC15H4esgUYVdmtXDxmQcIVIr92_F_3264_2448.jpg	3264 x 2448	1864px	57.11%	2039px	83.29%	47.57%
3➔	truck rectangle	Datacluster Truck (193).jpg	3264 x 2448	76px	2.33%	59px	2.41%	0.06%
4➔	truck rectangle	Datacluster Truck (193).jpg	3264 x 2448	604px	18.5%	542px	22.14%	4.1%
5➔	tractor rectangle	20210602_06_31_02_000_9WQPkiDGniaikLLMKfq2N6pTzGS2_F_4000_3000.jpg	4000 x 3000	933px	23.32%	1482px	49.4%	11.52%
6➔	truck rectangle	Datacluster Truck (6).jpg	4000 x 3000	1253px	31.32%	1571px	52.37%	16.4%
7➔	truck rectangle	Datacluster Truck (6).jpg	4000 x 3000	214px	5.35%	219px	7.3%	0.39%
8➔	tractor rectangle	20210601_14_36_21_000_Db2I2d17GLf53JzlQRSP28hD4Ml1_F_2592_1944.jpg	2592 x 1944	911px	35.15%	946px	48.66%	17.1%
9➔	tractor rectangle	20210530_06_33_50_000_kSBbVSEl9VV0elUslY8iz5Ua7N03_F_3264_2448.jpg	3264 x 2448	774px	23.71%	1512px	61.76%	14.65%
10➔	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 = { jul },
  note = { visited on 2024-07-27 },
}

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.

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