VADS-NON Object Detection Dataset and Pre-Trained Model by VADS

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1420 images

Explore Dataset

Here are a few use cases for this project:

Traffic Surveillance Systems: This computer vision model can be used to enhance traffic monitoring systems. It can accurately identify if a recorded scene represents an accident or non-accident scenario even under low light conditions.
Autonomous Vehicles: "VADS-NON" can be integrated into the system of autonomous vehicles. It can help the autonomous systems to understand if an accident occurred or if there's a safe, non-accident situation ahead, prompting it to react accordingly.
Insurance Claim Verification: The model could be utilized by insurance companies to verify claims accurately by analyzing scene images provided by customers. It could help determine whether an actual accident took place or if the event is likely a false claim.
Safety Training: Interactive safety training programs could use "VADS-NON" to help demonstrate the difference between accident and non-accident scenarios and teach how to respond properly in each circumstance.
Emergency Response Planning: The model can be used in emergency response planning. By studying different accident and non-accident scenarios, the system can help in formulating a more effective response plan.

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.

Cite This Project

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

@misc{
                            vads-non-kbdko_dataset,
                            title = { VADS-NON Dataset },
                            type = { Open Source Dataset },
                            author = { VADS },
                            howpublished = { \url{ https://universe.roboflow.com/vads/vads-non-kbdko } },
                            url = { https://universe.roboflow.com/vads/vads-non-kbdko },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { apr },
                            note = { visited on 2024-04-29 },
                            }