INFRARED Computer Vision Project
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.
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{ 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 2023-01-31 },
}