shangzx/High-Speed-Rear-End-Collision-Recognition-Dataset

High-Speed Rear-End Collision Recognition Dataset

The current transportation industry faces the challenge of frequent rear-end collisions. Timely and accurate identification and analysis of accidents have become urgent issues to be solved. Existing accident detection systems have significant deficiencies in recognition accuracy and real-time performance, failing to meet the needs of intelligent transportation management. This dataset aims to improve the accuracy and efficiency of accident recognition through extensive real-world images of rear-end collisions. Data collection utilizes high-definition cameras in various traffic environments, including urban roads and highways, ensuring data diversity and representativeness. In terms of quality control, a combination of multiple rounds of annotation and expert review is used to ensure the accuracy and consistency of data annotations. Data are stored in JPG format and classified by accident type for ease of subsequent processing and analysis. The core advantage of this dataset is its high data quality, with annotation precision exceeding 95%, and it boasts good consistency and completeness. By introducing new annotation methods, data processing efficiency has been improved, with an estimated reduction of 30% in annotation time. This dataset provides reliable data support for intelligent transportation systems, effectively enhancing the performance metrics of accident recognition systems and reducing the incidence of traffic accidents.

Technical Specifications

Field	Type	Description
file_name	string	File name
quality	string	Resolution
vehicle_count	int	The number of vehicles appearing in the image.
license_plate_visibility	boolean	Whether the license plate is clearly visible in the image.
collision_severity	string	Assessment of the severity of the rear-end collision in the image.
weather_condition	string	The weather conditions at the time the image was taken, such as sunny, rainy, or foggy.
road_condition	string	The condition of the road in the image, such as dry, slippery, or snowy.
time_of_day	string	The specific time of day when the image was taken, such as day, night, or dusk.
vehicle_type	string	Types of vehicles appearing in the image, such as cars, trucks, or motorcycles.
injury_severity	string	Assessment of potential injury severity in the vehicle accident.

Compliance Statement

Authorization Type	CC-BY-NC-SA 4.0 (Attribution–NonCommercial–ShareAlike)
Commercial Use	Requires exclusive subscription or authorization contract (monthly or per-invocation charging)
Privacy and Anonymization	No PII, no real company names, simulated scenarios follow industry standards
Compliance System	Compliant with China's Data Security Law / EU GDPR / supports enterprise data access logs

Source & Contact

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