depth-estimation Object Detection Dataset and Pre-Trained Model by P16

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Explore Dataset

Here are a few use cases for this project:

Road Maintenance Monitoring: This model can be used by government agencies or road construction companies to identify roads that need maintenance or repair. It can locate and categorize the types of damages to roads, helping to streamline maintenance efforts.
Traffic Safety Improvement: With its ability to quickly and accurately detect various road elements such as LMV, HMV, Unsurfaced Road, Speed Bump, etc., the model can be employed to identify unsafe conditions on roads, such as large potholes, damaged speed bumps, or sections of road surface that need improvement.
Fleet Management System: Commercial fleet managers can utilize this model to aid in planning and optimizing their vehicle routes. By getting insights about the conditions of the roads, they can avoid routes with poor road conditions or heavy traffic, leading to increased efficiency and reduced wear-and-tear on vehicles.
Autonomous Vehicle Navigation: Autonomous vehicles can utilize this model to gather data about the state of road surfaces. It can use this information to plot the safest and most efficient route, and smoothly navigate around road damages and other obstructions.
City Planning: City planners can use this model to monitor and analyze road conditions. This will aid in infrastructure development, helping to decide on where new roads or pedestrian paths should be constructed, or existing ones should be upgraded.

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{
                            depth-estimation_dataset,
                            title = { depth-estimation  Dataset },
                            type = { Open Source Dataset },
                            author = { P16 },
                            howpublished = { \url{ https://universe.roboflow.com/p16-81bkd/depth-estimation } },
                            url = { https://universe.roboflow.com/p16-81bkd/depth-estimation },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { aug },
                            note = { visited on 2024-04-28 },
                            }