CMU F1 Tenth: Cars and Walls Computer Vision Project
Updated a year ago
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F1TENTH: Towards Visual Understanding in Racing
This is the segmentation dataset for CMU F1TENTH Team 1's final project. In this project, we set out to detect F1TENTH track walls and opponent cars in the RealSense camera frames, apply semantic segmentation, and then generate a Birds-Eye View (BEV) occupancy grid.
This dataset includes images from both own ROS Bags and images provided by the University of Pennsylvania as part of the F1TENTH vision lab. Note that the annotations for this dataset could also be used to train an instance segmentation model, which we did and uploaded to Roboflow--that version is attached to the second version of this dataset and is also available for download here.
You can find the ONNX weights for the trained UNET model (semantic segmentation) here. This model was trained on the 2nd version of our dataset (V2) and achieved 93% recall, 97% precision, an F1 score of 0.95, and a mean IoU of 91%. We chose to train this semantic segmentation model on top of the yolov8 instance segmentation model as UNET is supported by the NVIDIA Isaac ROS image segmentation package, offering more efficient/optimized inference on Jetson Platforms.
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Cite This Project
If you use this dataset in a research paper, please cite it using the following BibTeX:
@misc{
cmu-f1-tenth-cars-and-walls_dataset,
title = { CMU F1 Tenth: Cars and Walls Dataset },
type = { Open Source Dataset },
author = { f1tenthsegmentationcarwall },
howpublished = { \url{ https://universe.roboflow.com/f1tenthsegmentationcarwall-ovq2k/cmu-f1-tenth-cars-and-walls } },
url = { https://universe.roboflow.com/f1tenthsegmentationcarwall-ovq2k/cmu-f1-tenth-cars-and-walls },
journal = { Roboflow Universe },
publisher = { Roboflow },
year = { 2023 },
month = { dec },
note = { visited on 2024-12-30 },
}