INFRARED Object Detection Dataset and Pre-Trained Model by Calib Buckton

Images

10228 images

Explore Dataset

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 2024-04-19 },
                            }