Road Damages Detection Computer Vision Project
Updated 3 months ago
The datasets of this project is sourced from Crowdsensing-based Road Damage Detection Challenge (CRDDC2022) (https://crddc2022.sekilab.global). The original challenge only considers four catergoires (classes) due to consistency of datasets collected. However, this project extends the utlitity of the datasets by confining dataset to the collection from Japan (containing classes of 7).
Here are a few use cases for this project:
Municipal Infrastructure Maintenance: City governments can use the "Road Damages Detection" model to efficiently identify and prioritize damaged roads for repair, leading to improved road safety and smoother traffic flow.
Pothole and Road Damage Reporting: Mobile app developers can integrate the model into their apps, allowing users to automatically detect and report road damage, such as potholes, directly to local authorities for faster repairs.
Insurance Claim Assessments: Insurance companies can use the model to analyze road conditions at the time of accidents, helping to determine liability and assess claim validity based on road damage influence on the incident.
Autonomous Vehicle Navigation: Autonomous vehicle systems can use the "Road Damages Detection" model to identify damaged roads ahead and adjust their routing or speed to optimize safety, comfort, and fuel efficiency.
Road Infrastructure Investment Planning: Transportation agencies can use the model to analyze current road conditions, enabling them to allocate funds and resources more effectively based on data-driven infrastructure improvement strategies.
Class of the Defects:
| Class Code | Damage Type |
|---|---|
| 0. D00 | Longitudinal Crack |
| 1. D10 | Transverse Crack |
| 2. D20 | Aligator Crack |
| 3. D40 | Pothole |
| 4. D43 | White Line Blur |
| 5. D44 | Cross Walk Blur |
| 6. D50 | Manhole Cover (TBC) |
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
road-damages-detection-xgcau_dataset,
title = { Road Damages Detection Dataset },
type = { Open Source Dataset },
author = { Ph },
howpublished = { \url{ https://universe.roboflow.com/ph-j6h7t/road-damages-detection-xgcau } },
url = { https://universe.roboflow.com/ph-j6h7t/road-damages-detection-xgcau },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2024 },
month = { aug },
note = { visited on 2024-11-29 },
}