Dhaka-AI - Dataset Ninja

Introduction #

ASM Shihavuddin, Mohammad Rifat Ahmmad Rashid

The creators of the Dhaka-AI: Dhaka Traffic Detection Challenge Dataset emphasize the distinctive traffic conditions in Dhaka city. Although it’s a city of significant size, only 7% of its roads meet urban standards. However, it contends with a staggering 8 million daily commuters, all within a 306 square kilometer area. In response to this complex challenge, they introduced the AI-based Dhaka Traffic Detection Challenge.

The primary objective of this challenge is to evaluate the effectiveness of advanced techniques in automated traffic detection using AI and ICT solutions, especially in the context of a diverse urban environment like Dhaka. Furthermore, the challenge seeks to encourage collaboration among experts in the region and establish a thriving AI-based community in South-East Asia.

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Summary #

Dhaka-AI: Dhaka Traffic Detection Challenge Dataset is a dataset for an object detection task. It is used in the urban planning research. Possible applications of the dataset could be in the vehicle detection domain.

The dataset consists of 3953 images with 24318 labeled objects belonging to 21 different classes including car, bus, motorbike, and other: three wheelers (CNG), rickshaw, truck, pickup, minivan, suv, van, bicycle, auto rickshaw, human hauler, wheelbarrow, ambulance, minibus, taxi, army vehicle, scooter, policecar, and garbagevan.

Images in the Dhaka-AI dataset have bounding box annotations. There are 956 (24% of the total) unlabeled images (i.e. without annotations). There are 3 splits in the dataset: train (3003 images), test (500 images), and test2 (450 images). The dataset was released in 2020.

Explore #

Dhaka-AI dataset has 3953 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.

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Have a look at 3953 images

View images along with annotations and tags, search and filter by various parameters

Class balance #

There are 21 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 1-10 of 21

Class ㅤ	Images ㅤ	Objects ㅤ	Count on image average	Area on image average
car➔ rectangle	1618	5467	3.38	5.47%
bus➔ rectangle	1554	3327	2.14	10.37%
motorbike➔ rectangle	1184	2280	1.93	4.23%
three wheelers (CNG)➔ rectangle	1167	2986	2.56	3.78%
rickshaw➔ rectangle	1019	3536	3.47	8.3%
truck➔ rectangle	842	1492	1.77	10.49%
pickup➔ rectangle	792	1224	1.55	5.67%
minivan➔ rectangle	575	934	1.62	3.33%
suv➔ rectangle	536	857	1.6	3.06%
van➔ rectangle	448	755	1.69	3.24%

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 1-10 of 21

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
car rectangle	5467	1.71%	53.59%	0%	7px	0.67%	2093px	75.56%	99px	10.15%	7px	0.42%	2539px	96.67%
rickshaw rectangle	3536	2.57%	77.87%	0.01%	7px	0.57%	2675px	98.61%	158px	15.87%	7px	0.48%	1914px	99.06%
bus rectangle	3327	4.91%	94.66%	0.01%	8px	0.77%	2641px	99.91%	191px	20.18%	9px	0.62%	3295px	99.95%
three wheelers (CNG) rectangle	2986	1.57%	44.19%	0%	8px	0.64%	2047px	76.39%	122px	11.48%	5px	0.32%	1542px	76.35%
motorbike rectangle	2280	2.22%	84.25%	0%	8px	0.5%	1568px	89.12%	131px	11.37%	4px	0.24%	1917px	99.84%
truck rectangle	1492	6%	98.84%	0.01%	7px	1.05%	3086px	99.83%	215px	22.52%	10px	0.72%	3735px	99.58%
pickup rectangle	1224	3.71%	83.89%	0.01%	8px	1.06%	2187px	99.69%	148px	17.73%	10px	0.5%	3692px	95.82%
minivan rectangle	934	2.06%	47.62%	0.01%	11px	0.69%	894px	82.13%	110px	11.79%	12px	0.75%	1368px	71.25%
suv rectangle	857	1.92%	75.32%	0.01%	11px	0.64%	1205px	98.98%	133px	11.11%	11px	0.67%	1558px	82.55%
van rectangle	755	1.95%	54.1%	0.01%	8px	0.93%	1251px	80.37%	117px	11.54%	10px	0.6%	1518px	79.06%

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 24318 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 24318

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➔	motorbike rectangle	Navid_239.jpg	1080 x 1920	157px	14.54%	115px	5.99%	0.87%
2➔	motorbike rectangle	Navid_239.jpg	1080 x 1920	86px	7.96%	91px	4.74%	0.38%
3➔	motorbike rectangle	Navid_239.jpg	1080 x 1920	70px	6.48%	54px	2.81%	0.18%
4➔	motorbike rectangle	Navid_239.jpg	1080 x 1920	86px	7.96%	60px	3.12%	0.25%
5➔	bicycle rectangle	Navid_239.jpg	1080 x 1920	126px	11.67%	117px	6.09%	0.71%
6➔	car rectangle	Navid_239.jpg	1080 x 1920	132px	12.22%	150px	7.81%	0.95%
7➔	car rectangle	Navid_239.jpg	1080 x 1920	93px	8.61%	185px	9.64%	0.83%
8➔	suv rectangle	Navid_239.jpg	1080 x 1920	108px	10%	173px	9.01%	0.9%
9➔	truck rectangle	Pias (382).jpg	720 x 1280	237px	32.92%	251px	19.61%	6.45%
10➔	bus rectangle	Navid_292.jpg	720 x 1280	211px	29.31%	188px	14.69%	4.3%

License #

Dhaka Traffic Detection Challenge Dataset is under GNU GPL 2.0 license.

Citation #

If you make use of the Dhaka-AI data, please cite the following reference:

@dataset{Dhaka-AI,
  author={ASM Shihavuddin and Mohammad Rifat Ahmmad Rashid},
  title={Dhaka Traffic Detection Challenge Dataset},
  year={2020},
  url={https://www.kaggle.com/datasets/rifat963/dhakaai-dhaka-based-traffic-detection-dataset}
}

Source

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

@misc{ visualization-tools-for-dhaka-ai-dataset,
  title = { Visualization Tools for Dhaka-AI Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/dhaka-ai } },
  url = { https://datasetninja.com/dhaka-ai },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { nov },
  note = { visited on 2024-11-21 },
}

Download #

Dataset Dhaka-AI 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='Dhaka-AI', 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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