Semantic Segmentation

LaneSegMay05 Computer Vision Project


993 images
Explore Dataset

Here are a few use cases for this project:

  1. Autonomous Driving: This model can be used to identify lane lines in real-time to support an autonomous car's navigation system to stay within the correct lane and make safer turns and lane changes.

  2. Traffic Monitoring: "LaneSegMay05" could be implemented by traffic monitoring systems to recognize lane lines accurately and monitor traffic flow or detect infractions like cars crossing the lines irrationally.

  3. Virtual Driver Assistant Applications: The model could be used in creating applications that alert drivers if they are veering out of their lane, enhancing road safety.

  4. Road Maintenance: Government and road authorities could use this model to identify areas where lane lines have faded or been damaged, helping to prioritize road maintenance tasks.

  5. Simulator Training: It could be used in driving simulators to create more realistic and accurate depictions of roads and lanes for driver training purposes.

Please note, the model might need additional training or customization as the provided example image doesn't seem to align with typical lane line identification scenarios. The described dataset instead features images of a man, which doesn't directly relate to lane identity.

Cite this Project

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

@misc{ lanesegmay05_dataset,
    title = { LaneSegMay05 Dataset },
    type = { Open Source Dataset },
    author = { pi },
    howpublished = { \url{ https://universe.roboflow.com/pi-gojg3/lanesegmay05 } },
    url = { https://universe.roboflow.com/pi-gojg3/lanesegmay05 },
    journal = { Roboflow Universe },
    publisher = { Roboflow },
    year = { 2023 },
    month = { may },
    note = { visited on 2023-12-02 },

Find utilities and guides to help you start using the LaneSegMay05 project in your project.



Last Updated

6 months ago

Project Type

Semantic Segmentation




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