Real-time Road Anomalies Detection in Different Weather conditions and Lightning Object Detection Dataset and Pre-Trained Model by Pothole Detection

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Here are a few use cases for this project:

Road Safety Improvement: Government road maintenance departments or highway authorities can use this model to proactively identify and fix road anomalies, thus dramatically improving road safety and comfort for all road users.
Autonomous Vehicles: This model could be integrated into the systems of self-driving cars. It would allow these vehicles to accurately detect road anomalies in real-time and navigate around them appropriately, ensuring a safer and smoother journey.
Ride-Share Companies: Companies like Uber or Lyft could use this model to gather data on the condition of roads used by their drivers, and then prioritize routes with fewer road anomalies for the comfort and safety of their passengers.
Dynamic Navigation and Mapping Apps: Real-time road anomalies detection could be used to update navigation apps like Google Maps or Waze. This would provide real-time alerts about road conditions to users and suggest alternative routes to avoid problematic areas.
Infrastructure Maintenance: Urban planners and city maintenance departments could use this model as a tool to monitor urban infrastructure. It would assist in identifying areas requiring maintenance promptly, thus efficiently planning their repair and maintenance schedules.

Trained Model API

This project has a trained model available that you can try in your browser and use to get predictions via our Hosted Inference API and other deployment methods.

YOLOv8

This project has a YOLOv8 model checkpoint available for inference with Roboflow Deploy. YOLOv8 is a new state-of-the-art real-time object detection model.

Cite This Project

If you use this dataset in a research paper, please cite it using the following BibTeX:

@misc{
                            real-time-road-anomalies-detection-in-different-weather-conditions-and-lightning_dataset,
                            title = { Real-time Road Anomalies Detection in Different Weather conditions and Lightning Dataset },
                            type = { Open Source Dataset },
                            author = { Pothole Detection },
                            howpublished = { \url{ https://universe.roboflow.com/pothole-detection-1nczj/real-time-road-anomalies-detection-in-different-weather-conditions-and-lightning } },
                            url = { https://universe.roboflow.com/pothole-detection-1nczj/real-time-road-anomalies-detection-in-different-weather-conditions-and-lightning },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-05-02 },
                            }

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