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.