# Free-Space Detection with Self-Supervised and Online Trained Fully Convolutional Networks

arXiv: https://arxiv.org/abs/1604.02316 · HTML: https://mlsanity.com/paper/1604.02316/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Apr 2016.

## Built on by (most cited first, 3 of 3)

- [NVAutoNet: Fast and Accurate 360circ 3D Visual Perception For Self Driving](https://mlsanity.com/paper/2303.12976/) (2023)
- [Training Semantic Segmentation on Heterogeneous Datasets](https://mlsanity.com/paper/2301.07634/) (2023)
- [StixelNExT: Toward Monocular Low-Weight Perception for Object Segmentation and Free Space Detection](https://mlsanity.com/paper/2407.08277/) (2024)

## Abstract (arXiv)

Recently, vision-based Advanced Driver Assist Systems have gained broad interest. In this work, we investigate free-space detection, for which we propose to employ a Fully Convolutional Network (FCN). We show that this FCN can be trained in a self-supervised manner and achieve similar results compared to training on manually annotated data, thereby reducing the need for large manually annotated training sets. To this end, our self-supervised training relies on a stereo-vision disparity system, to automatically generate (weak) training labels for the color-based FCN. Additionally, our self-supervised training facilitates online training of the FCN instead of offline. Consequently, given that the applied FCN is relatively small, the free-space analysis becomes highly adaptive to any traffic scene that the vehicle encounters. We have validated our algorithm using publicly available data and on a new challenging benchmark dataset that is released with this paper. Experiments show that the online training boosts performance with 5% when compared to offline training, both for Fmax and AP.