HeteroTraffic: Annotated Dataset for Multi-Class Vehicle Detection in Varied Illumination and Road Conditions

The HeteroTraffic dataset is a large-scale, multi-class dataset developed for heterogeneous vehicle detection and intelligent transportation research. It contains 17,310 high-resolution images, each annotated in YOLO format to facilitate training and evaluation of object detection models such as YOLOv8, YOLOv11, and EfficientDet.

The images were collected from real-world highway and roadside environments under diverse traffic densities, weather conditions, and illumination variations. Data were acquired using DSLR cameras and smartphones, ensuring a mix of perspectives and resolutions that reflect realistic driving and surveillance scenarios.An additional version of the dataset has been uploaded where all faces and vehicle license plates are blurred to ensure privacy compliance and ethical data sharing.

Each image in the dataset is paired with an annotation file containing bounding boxes and class identifiers generated through LabelMe, later converted to YOLO format. The dataset includes 17 heterogeneous classes that represent a wide variety of vehicles and road users commonly found in mixed-traffic environments.

Vehicle Classes:
Motorbike, MPV, Pedestrian, Pickup, PowerTiller, Rickshaw, Bicycle, Bus, Bhotbhoti, Car, CNG, Easybike, Leguna, ShoppingVan, Truck, Van, and Wheelbarrow.

This diversity makes HeteroTraffic particularly suitable for developing models capable of distinguishing between both conventional and region-specific vehicle types, enhancing real-world generalization in computer vision applications.

Dataset Structure:
HeteroTraffic/
│
├── images/
│ ├── *.jpg
│
├── labels/
│ ├── *.txt
│
└── data.yaml

Each .txt file in the labels directory contains YOLO-formatted annotations:

Key Features:
Total Images: 16,289
Annotation Format: YOLO (converted from LabelMe JSON)
Number of Classes: 17
Data Type: RGB road and highway scenes
Image Sources: DSLR and smartphone cameras
Annotation Verification: Manually reviewed for accuracy
Use Cases: Vehicle detection, traffic monitoring, intelligent transportation, and autonomous driving

Highlights
Provides diverse, heterogeneous vehicle categories including low-frequency and region-specific types.
Enables benchmarking of deep learning models under real-world highway conditions.
Supports transfer learning, domain adaptation, and object detection studies.
Serves as a high-quality open resource for both academic and industrial research.