Train detection Object Detection Dataset and Pre-Trained Model by ZARIZO

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Explore Dataset

Here are a few use cases for this project:

Railway Maintenance and Detection System: The model can be utilized for predictive maintenance by identifying inconsistencies in the trains, allowing for early detection of potential problems like broken fuel pipes or malfunctioning doors.
Safety Monitoring: The model can be deployed on surveillance systems at railway stations and crossings to detect and classify the details of incoming trains, such as whether the bridge is up or down, thus ensuring the safety of pedestrians and vehicles.
Railway Traffic Management: The system can aid in creating more efficient railway schedules by detecting the presence and type of trains, helping avoid conflicts and improving overall efficiency.
Augmented Reality Edutainment: The model can be used in AR apps about trains or railway systems, aiding in the visual identification and classification of various parts of the train, enhancing the learning and interactive experience.
Industrial Logistics Management: Logistic companies that move goods via rail can use the model to monitor and identify specific train compartments (like fuel pipe, cistern, open, or close) to improve their loading and unloading processes, optimizing efficiency.

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{
                            train-detection-plrge_dataset,
                            title = { Train detection Dataset },
                            type = { Open Source Dataset },
                            author = { ZARIZO },
                            howpublished = { \url{ https://universe.roboflow.com/zarizo/train-detection-plrge } },
                            url = { https://universe.roboflow.com/zarizo/train-detection-plrge },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2023 },
                            month = { may },
                            note = { visited on 2024-06-18 },
                            }