DOI: 10.5281/zenodo.18278988
title: 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.
description: 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) 
description: 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
description: 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;