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End of preview. Expand
in Data Studio
CCTV Traffic Accident Object Detection Dataset
Overview
This dataset contains low-resolution CCTV traffic surveillance images annotated with bounding boxes for traffic accident localization.
Each image includes regions corresponding to:
- Accident: vehicles or regions directly involved in a collision or traffic incident
- Non-accident: visible vehicles or regions not involved in the incident
The dataset is designed to support fine-grained object detection under real-world surveillance constraints, such as compression artifacts, motion blur, and fixed camera viewpoints.
Dataset Composition
- Total images: 1,485 unique CCTV frames
- Expanded images: ~3,000 images after offline augmentation
- Resolution: 640 × 640 (letterboxed)
- Annotation format: COCO-style bounding boxes
- Classes:
accidentnon_accident
Each image may contain multiple objects, including both accident and non-accident regions, reflecting realistic traffic scenes.
Annotation Logic
Bounding boxes are defined as follows:
accident
Regions corresponding to vehicles or areas directly involved in a traffic collision or incident.non_accident
Vehicles or regions present in the scene but not involved in the accident event.
This design enables context-aware accident localization, where the model must distinguish involved and uninvolved objects within the same frame.
Data Source
The original dataset was published on Roboflow Universe as the
“Accident and Non-accident label Image Dataset”.
- Original source:
https://universe.roboflow.com/accident-and-nonaccident/accident-and-non-accident-label-image-dataset
This Hugging Face version provides:
- cleaned category structure
- standardized splits (train / validation / test)
- consistent class indexing
- ready-to-use object detection schema
Preprocessing & Augmentation
The following preprocessing and augmentation were applied offline during dataset creation:
- Resize to 640×640 with padding
- Horizontal flip (50% probability)
- Random brightness adjustment (±20%)
⚠️ No additional augmentations are applied at training time by default.
Users are encouraged to account for existing augmentations when designing training pipelines.
Intended Use
This dataset is suitable for:
- Traffic accident localization
- CCTV-based surveillance analysis
- Safety and incident monitoring systems
- Research on object detection under low-quality visual conditions
- Benchmarking transformer-based detectors (e.g., DETR-style models)
Limitations
Users should be aware of the following limitations:
- Small-to-medium scale dataset
- Fixed camera viewpoints
- Low-resolution and compressed CCTV imagery
- Limited geographic and environmental diversity
As such, models trained on this dataset should not be assumed to generalize broadly without further validation.
License
This dataset is released under the
Creative Commons Zero v1.0 Universal (CC0-1.0) license.
The original dataset was designated as Public Domain by its authors.
Citation
If you use this dataset in academic research, please cite the original dataset:
@misc{
accident-and-non-accident-label-image-dataset_dataset,
title = { Accident and Non-accident label Image Dataset Dataset },
type = { Open Source Dataset },
author = { Accident and Nonaccident },
howpublished = { \url{ https://universe.roboflow.com/accident-and-nonaccident/accident-and-non-accident-label-image-dataset } },
url = { https://universe.roboflow.com/accident-and-nonaccident/accident-and-non-accident-label-image-dataset },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { dec }
}
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