INFRARED Computer Vision Project

Calib Buckton

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Description

This infrared street scenes computer vision dataset labels cars, people, bicycles, and dogs (although dogs are severely under-represented; see the health check for more info on class distribution). It contains over 10,000 grayscaled thermal images taken with a FLIR camera that can be used for detecting objects at night or in poor lighting conditions.

It may be particularly useful in creating self-driving autonomous vehicles, due to the forward-looking dashcam style footage of roads, pedestrians, and vehicles. There is also a pre-trained model available for testing.

Example use-cases include:

  • Prototyping a self-driving vehicle
  • Creating a tool that warns drivers if pedestrians are about to cross their path
  • Counting cars driving by (or people walking by) at night
  • Intelligently turning lights on and off for people and vehicles (but not animals)
  • Detecting unauthorized access to unlit facilities

You could also re-label other objects of interest from this dataset to train a model to detect other types of things.

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Cite This Project

LICENSE
CC BY 4.0

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

                        @misc{
                            infrared-6s0ke_dataset,
                            title = { INFRARED Dataset },
                            type = { Open Source Dataset },
                            author = { Calib Buckton },
                            howpublished = { \url{ https://universe.roboflow.com/calib-buckton/infrared-6s0ke } },
                            url = { https://universe.roboflow.com/calib-buckton/infrared-6s0ke },
                            journal = { Roboflow Universe },
                            publisher = { Roboflow },
                            year = { 2022 },
                            month = { sep },
                            note = { visited on 2024-10-07 },
                            }
                        
                    

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