Road Towing and Accident Handling Dataset

#target detection #image segmentation #road monitoring #accident analysis #traffic management
  • 15000 records
  • 3.2G
  • JPG/PNG/JSON
  • CC-BY-NC-SA 4.0
  • MOBIUSI INCMOBIUSI INC
Updated:2026-03-02

AI Analysis & Value Prop

The current transportation industry faces numerous challenges in road safety management, particularly in accident handling and towing dispatch efficiency. Traditional methods often rely on manual judgment and experience, which are inefficient and prone to errors. Existing solutions mostly involve static monitoring, lacking real-time responsiveness and intelligent analysis. The construction of this dataset aims to enhance the accuracy and timeliness of target detection technology by providing comprehensive road towing and accident data. The dataset includes images under various weather and lighting conditions to reflect real scenarios. Data collection employs high-definition camera equipment for around-the-clock monitoring on major city roads and accident-prone areas, ensuring data diversity and representativeness. Regarding quality control, a multi-round annotation mechanism was employed, and consistency checks were conducted by professionals to ensure annotation accuracy. Data is stored in JPEG format, organized chronologically, facilitating subsequent analysis and processing.

Dataset Insights

Sample Examples

b316c141**.jpg|4032*3024|1.75 MB

bc1cfe74**.jpg|4032*3024|626.40 KB

Technical Specifications

Compliance Statement

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

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Cite this Work

@dataset{Mobiusiundefined,
  title={},
  author={Mobiusi},
  year={undefined},
  url={https://www.mobiusi.com/datasets/780e728fd5fe3b40b467923d70d66e67?dataset_scene_id=1},
  urldate={},
  keywords={},
  version={}
}

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