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:
<class_id> <x_center> <y_center> <width> <height>

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.

异构交通（HeteroTraffic）数据集是为异构车辆检测与智能交通研究打造的大规模多类别数据集。该数据集包含17310张高分辨率图像，所有图像均采用YOLO格式标注，可便捷用于YOLOv8、YOLOv11、EfficientDet等目标检测模型的训练与评估。

图像采集自真实公路与路侧场景，涵盖多样的交通密度、天气条件与光照变化。数据通过数码单反相机（DSLR）与智能手机采集，兼顾不同拍摄视角与分辨率，贴合真实驾驶与监控场景。此外，数据集还上传了隐私合规版本，该版本中所有人脸与车辆牌照均已做模糊处理，以保障数据共享的合规性与伦理性。

数据集中每张图像均配有标注文件，标注文件包含由LabelMe生成并后续转换为YOLO格式的边界框与类别标识符。该数据集涵盖17个异构类别，覆盖混合交通场景中常见的各类车辆与道路使用者。

车辆类别：
摩托车（Motorbike）、多用途汽车（MPV）、行人（Pedestrian）、皮卡（Pickup）、动力耕耘机（PowerTiller）、人力三轮车（Rickshaw）、自行车（Bicycle）、巴士（Bus）、Bhotbhoti、轿车（Car）、CNG车辆、电动自行车（Easybike）、Leguna、购物货车（ShoppingVan）、卡车（Truck）、厢式货车（Van）、手推车（Wheelbarrow）。

这种类别多样性使得异构交通数据集尤其适用于开发可区分常规车辆与区域特色车型的模型，进而提升计算机视觉应用在真实场景中的泛化能力。

数据集目录结构：
HeteroTraffic/
│
├── images/
│ ├── *.jpg
│
├── labels/
│ ├── *.txt
│
└── data.yaml

标注目录下的每个.txt文件均采用YOLO格式的标注格式，格式为：<class_id> <x_center> <y_center> <width> <height>

核心特性：
图像总数：16289
标注格式：YOLO（由LabelMe JSON格式转换而来）
类别数量：17
数据类型：RGB格式的道路与公路场景图像
图像采集设备：数码单反相机（DSLR）与智能手机
标注校验：经人工审核以确保标注准确性
应用场景：车辆检测、交通监控、智能交通与自动驾驶

数据集亮点：
1. 提供涵盖低频车型与区域特色车型在内的多样化异构车辆类别；
2. 支持在真实公路场景下对深度学习模型进行性能基准测试；
3. 可用于迁移学习、域自适应与目标检测相关研究；
4. 可为学术与工业界研究提供高质量的开源数据集资源。