Dataset Ninja LogoDataset Ninja:

ADAS Dataset

90392001
Tagself-driving
Taskobject detection
Release YearMade in 2022
Licenseunknown

Introduction #

Prabhu Somsai Talari, Pallavi Ramicetty

ADAS: Dataset for Advance Driver Assistant Systems is designed for object detection in Advanced Driver Assistance Systems, comprising 903 images and 1143 labeled objects across 9 classes like pothole, left_hand_curve, right_hand_curve, and other: speed_breaker, bridge_ahead, pedestrian, animal, name_board, and vehicle. The dataset covers diverse scenarios, including different road types, times of day, and capture angles. Notably, images were taken with two phone models, iPhone 12 and VIVO, ensuring variability for robust model evaluation.

Based on the exploration conducted, the author identified a shortage of datasets catering to solving Driver Assistant use cases, encompassing critical tasks like road sign detection, pedestrian detection, vehicle detection, animal detection, pothole detection, speed breaker detection, and more.

image

Different scenarios that authors considered:

  • Different type of roads (Highways, town roads, street/village roads)
  • Different time (Darkness,Sunny, Rainy, Fog, crowded place and cloudy)
  • Maintain distance/different angles to capture objects on the road.
image

Also used 2 different configuration phones to capture images. (iphone 12 and VIVO)

image

Authors labelled data using the β€œlabelme” tool. There are considered labels, across all categories:

  • animal
  • pedestrian
  • name_board
  • speed_beaker
  • pothole
  • right_hand_curve
  • bridge_ahead
  • left_hand_curve
ExpandExpand
Dataset LinkHomepage

Summary #

ADAS: Dataset for Advance Driver Assistant Systems is a dataset for an object detection task. Possible applications of the dataset could be in the automotive industry.

The dataset consists of 903 images with 1143 labeled objects belonging to 9 different classes including pothole, left_hand_curve, right_hand_curve, and other: speed_breaker, bridge_ahead, pedestrian, animal, name_board, and vehicle.

Images in the ADAS dataset have bounding box annotations. There are 62 (7% of the total) unlabeled images (i.e. without annotations). There are no pre-defined train/val/test splits in the dataset. Also, the dataset contains id, camera and category tags. Pothole category with iPhone 12 camera tag also includes road_type tag. The dataset was released in 2022.

Dataset Poster

Explore #

ADAS dataset has 903 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 ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
OpenSample annotation mask from ADASSample image from ADAS
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Have a look at 903 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 9 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 1-9 of 9
Class
γ…€
Images
γ…€
Objects
γ…€
Count on image
average
Area on image
average
potholeβž”
rectangle
369
607
1.64
2.61%
left_hand_curveβž”
rectangle
111
111
1
0.2%
right_hand_curveβž”
rectangle
107
107
1
0.26%
speed_breakerβž”
rectangle
96
96
1
4.69%
bridge_aheadβž”
rectangle
70
70
1
0.54%
pedestrianβž”
rectangle
58
88
1.52
3.37%
animalβž”
rectangle
15
44
2.93
5.2%
name_boardβž”
rectangle
12
12
1
1.06%
vehicleβž”
rectangle
8
8
1
1.29%

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 1-9 of 9
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
pothole
rectangle
607
1.59%
24.39%
0.01%
24px
0.8%
1399px
46.63%
250px
8.31%
58px
1.45%
2835px
70.31%
left_hand_curve
rectangle
111
0.2%
0.87%
0.02%
51px
1.69%
280px
9.26%
133px
4.35%
53px
1.31%
377px
9.35%
right_hand_curve
rectangle
107
0.26%
0.71%
0.07%
78px
2.6%
301px
10.03%
152px
5.04%
101px
2.52%
348px
8.63%
speed_breaker
rectangle
96
4.69%
19.13%
0.43%
46px
1.53%
790px
26.33%
246px
8.18%
1038px
25.95%
3792px
94.8%
pedestrian
rectangle
88
2.22%
13.27%
0.2%
189px
6.25%
1904px
63.47%
704px
23.25%
86px
2.13%
957px
23.93%
bridge_ahead
rectangle
70
0.54%
14.95%
0.1%
104px
3.47%
512px
17.07%
240px
7.87%
78px
1.95%
3504px
87.6%
animal
rectangle
44
1.78%
11.5%
0.09%
109px
3.63%
1175px
39.17%
455px
15.11%
98px
2.43%
1334px
33.09%
name_board
rectangle
12
1.06%
3.21%
0.39%
173px
5.77%
598px
19.78%
286px
9.47%
273px
6.83%
654px
16.22%
vehicle
rectangle
8
1.28%
2.58%
0.31%
222px
7.34%
648px
21.43%
395px
13.07%
169px
4.19%
630px
15.62%

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 1143 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 1143
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”
pothole
rectangle
IMG_3652.jpg
3024 x 4032
333px
11.01%
1072px
26.59%
2.93%
2βž”
pothole
rectangle
IMG_20221106_112423.jpg
3000 x 4000
187px
6.23%
436px
10.9%
0.68%
3βž”
pothole
rectangle
IMG_3678.jpg
3024 x 4032
228px
7.54%
637px
15.8%
1.19%
4βž”
bridge_ahead
rectangle
IMG_3896.jpg
3024 x 4032
307px
10.15%
192px
4.76%
0.48%
5βž”
pothole
rectangle
IMG_20221106_122526.jpg
3000 x 4000
569px
18.97%
696px
17.4%
3.3%
6βž”
right_hand_curve
rectangle
road_sign_IMG_3596.jpg
3024 x 4032
228px
7.54%
348px
8.63%
0.65%
7βž”
pothole
rectangle
IMG_20221106_113403.jpg
3000 x 4000
123px
4.1%
338px
8.45%
0.35%
8βž”
bridge_ahead
rectangle
IMG_20221112_044545.jpg
3000 x 4000
149px
4.97%
107px
2.67%
0.13%
9βž”
right_hand_curve
rectangle
IMG_20221112_045919.jpg
3000 x 4000
96px
3.2%
141px
3.52%
0.11%
10βž”
pothole
rectangle
IMG_20221106_123505.jpg
3000 x 4000
273px
9.1%
461px
11.53%
1.05%

License #

License is unknown for the Dataset for Advance Driver Assistant Systems (ADAS) dataset.

Source

Citation #

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

@dataset{ADAS,
  author={Prabhu Somsai Talari and Pallavi Ramicetty},
  title={Dataset for Advance Driver Assistant Systems (ADAS)},
  year={2022},
  url={https://www.kaggle.com/datasets/prabhusomsaitalari/dataset-for-driver-assistant-ml-models}
}

Source

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

@misc{ visualization-tools-for-adas-dataset,
  title = { Visualization Tools for ADAS Dataset },
  type = { Computer Vision Tools },
  author = { Dataset Ninja },
  howpublished = { \url{ https://datasetninja.com/adas } },
  url = { https://datasetninja.com/adas },
  journal = { Dataset Ninja },
  publisher = { Dataset Ninja },
  year = { 2024 },
  month = { jul },
  note = { visited on 2024-07-25 },
}

Download #

Please visit dataset homepage to download the data.

. . .

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