Published February 4, 2026 | Version 1.0.0
Dataset Open

DRASHTI-HaOBB: Drone nadiR-view Annotated imageS of veHicles dataseT for India - Heading-angle Oriented Bounding Box: Indian Vehicle Oriented-Object-Detection Dataset with 1.3 Million Samples

Description

DRASHTI-HaOBB (Drone nadiR-view Annotated imageS of veHicles dataseT for India - Heading-angle Oriented Bounding Box): Indian Vehicle Oriented-Object-Detection Dataset with 1.3 Million Vehicle Samples

Data collectors & curators: Yagnik Bhavsar, Mazad Zaveri, Mehul Raval, Shaheriar Zaveri

  • Annotations: Oriented Bounding Box (OBB) and Heading-angle for Vehicles; Flight-height 
  • View: Drone-based Aerial-view (90° downward gimbal)
  • Location: Ahmedabad, Gujarat, India
  • No. of Vehicle Classes: 14
    • Class Labels: ‘Auto3WCargo’, ‘AutoRicksaw’, ‘Bus’, ‘Container’, ‘Mixer’, ‘MotorCycle’, ‘PickUp’, ‘SUV’, ‘Sedan’, ‘Tanker’, ‘Tipper’, ‘Trailer’, ‘Truck’, ‘Van’
  • No. of  total Images: 27,577 (84.73% are real-world images and 15.27% are augmented* images)   
    • No. of  total (vehicle) Samples: 1,308,989
  • Dimension of each 4K image: 3840x2160
  • AI - Computer Vision Task: Oriented Object Detection - (Compatible with Ultralytics YOLO and OpenMMLab MMRotate)
  • Classwise distribution of the dataset: 
    Split Train Val Test
    Class Real Augmented Real Augmented Real Augmented
    Auto3WCargo 8652 9888 4394 4934 1519 1732
    AutoRicksaw 56836 14279 28608 7189 9543 2522
    Bus 1363 13864 666 7106 260 2045
    Container 7429 9397 3766 4576 1179 1791
    Mixer 1752 4355 829 2217 285 740
    MotorCycle 323856 3115 162306 1492 53557 592
    PickUp 19284 7147 9497 3610 3167 1238
    SUV 164457 1293 82274 676 27042 248
    Sedan 46188 810 23058 423 7605 155
    Tanker 5900 6014 2930 2981 966 1145
    Tipper 9576 4043 4690 2014 1555 734
    Trailer 6116 8026 3075 3880 1005 1423
    Truck 25240 11851 12657 5703 4261 1915
    Van 10644 4015 5288 2072 1750 714

*Data augmentation uses "copy-paste" technique (Ghiasi et al., CVPR 2021), where additional samples of minority vehicle classes are pasted on real-world images containing parked vehicles, to mitigate the class imbalance problem.

Notes

Dataset structure:

After downloading all six ZIP files, the dataset must be reorganised into the directory format described below. Each image directory contains JPEG images, and the corresponding label directory contains annotations in a text files.

DRASHTI-HaOBB/

  • images/
    • train/ *.jpg
    • val/ *.jpg
    • test/ *.jpg
  • labels/
    • train_original/ *.txt
    • val_original/ *.txt
    • test_original/ *.txt

Description of the label (txt) file:

  • The first line in the txt file specifies the flight height of the drone for the particular image
  • Each of the remaining lines in the txt file has the following info in that order:
    • x1, y1, x2, y2, x3, y3, x4, y4, class-label, difficulty-level, heading-angle
  • The four points of the Oriented Bounding Box (polygon) of the vehicle are given by x1, y1, x2, y2, x3, y3, x4, y4
  • A class-label could be one of the following 14 classes: ‘Auto3WCargo’, ‘AutoRicksaw’, ‘Bus’, ‘Container’, ‘Mixer’, ‘MotorCycle’, ‘PickUp’, ‘SUV’, ‘Sedan’, ‘Tanker’, ‘Tipper’, ‘Trailer’, ‘Truck’, ‘Van’
  • A difficulty level could have either 0 or 1
    • 1 would indicate a cropped vehicle on the boundary of the image (indicating that it is hard to figure out the class of the vehicle, because it is only partially visible in the image)
  • Heading-angle (in the range 0 to 359 degrees) provides the clockwise angle (w.r.t to the x-axis of the image coordinate system) of the vehicle's front (indicating the direction of motion of the vehicle) 

Notes

Description of the meta-level CSV file (DRASHTI-HaOBB_framewise_info.csv):

  • Each row contains the following info:
    • Image name, Split (Image is part of which set: training/test/validation), Image is real or augmented (Augmented: Yes/No), and the number of vehicle samples in the Image under each of the 14 classes

Notes

List of known issues/observations:

  • The dataset obviously consists of cropped vehicle samples (on the boundary of the image) because these vehicles are entering/leaving the scene. 
    • Such severely cropped vehicles on the boundary of the image, having one of the HBB (for the OBB) dimensions smaller than ~20 pixels, are not annotated, because they do not contain any usable visible information
    • For a few such cropped vehicles, especially on the boundary of the image, the OBB may not be a rectangle (but would still be a 4-point polygon)
  • In the Auto3WCargo class, there are very few samples (~200) of non-motorised tricycles; This particular object was annotated/included because its cargo compartment is large and looks like an Auto3WCargo
  • Sometimes, AutoRicksaw are carrying cargo-like objects on the top of the vehicle, and may appear like Auto3WCargo. Such vehicles are correctly classified as AutoRicksaw in the annotations
  • In the Trailer class, there are a few samples (~2500) of agriculture tractor-trailer; There are also ~50 samples of only agriculture tractor
  • Very few vehicles may be occluded by trees or by traffic light poles
  • Few images are under smog and dusty locations (and the vehicles appear blurry), especially near Ready-Mix Cement sites, and sand/mud-filling stations
  • The heading angle for parked vehicles is approximate
  • The heading angle for the vehicle just entering / about to leave the image (scene) is approximate
  • For the MotorCycle class, a very small percentage of bounding boxes would be misaligned; Also, sometimes, pedestrians with nearby shadows may look like a MotorCycle, and would have been inadvertently annotated as a MotorCycle
  • Pedestrians and non-motorised objects (example: bicycles or tricycles) are not annotated
  • During the final visual validation of the dataset using the tool Visual Similarity Duplicate Image Finder, we found total errors to be extremely small percentage (0.038 %) [~500 samples out of total 1.3 million samples],  which include (human errors): misaligned (or lagging) OBBs for Truck, SUV, AutoRicksaw and MotorCylce; un-annotated MotorCycle; empty OBBs for MotorCycle;

Files

DRASHTI-HaOBB_framewise_info.csv

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Additional details

Related works

Is described by
Journal article: 10.1109/ieeedata.2026.3670752 (DOI)

Funding

Ahmedabad University
Seed Grant URBSEASI21A3
Gujarat Council on Science and Technology
Research Project Grant GUJCOST/STI/2021–22/3858

Dates

Created
2026-02-04

Software

References

  • Y. M. Bhavsar, M. S. Zaveri, M. S. Raval and S. B. Zaveri, "Evaluating defensive driving behaviour based on safe distance between vehicles: A case study using computer vision on UAV videos at urban roundabout", Multimodal Transportation, Elsevier, 2025. DOI: https://doi.org/10.1016/j.multra.2025.100227
  • Y. M. Bhavsar, M. S. Zaveri, M. S. Raval and S. B. Zaveri, "Vision-based investigation of road traffic and violations at urban roundabout in India using UAV video: A case study", Transportation Engineering, Elsevier, 2023. DOI: https://doi.org/10.1016/j.treng.2023.100207
  • Y. M. Bhavsar, M. S. Zaveri, M. S. Raval and S. B. Zaveri, "U-UTM: A Cyber-Physical System for Road Traffic Monitoring Using UAVs," 2024 IEEE International Conference on Vehicular Electronics and Safety (ICVES), Ahmedabad, India, 2024, DOI: https://doi.org/10.1109/ICVES61986.2024.10927911
  • Y. M. Bhavsar, M. S. Zaveri, M. S. Raval, K. R. Patel and S. B. Zaveri, "Descriptor: Drone Nadir-view Annotated Images of Vehicles Detection Dataset for India with Heading-angle Oriented Bounding Box (DRASHTI-HaOBB)," in IEEE Data Descriptions, March 2026, DOI: https://doi.org/10.1109/ieeedata.2026.3670752