Where to drive: free space detection with one fisheye camera

TL;DR

This paper proposes using synthetic fisheye images generated in Unity3D to train deep learning models for free space detection in autonomous driving, addressing data scarcity issues.

Contribution

It introduces a novel method to generate synthetic fisheye training data and evaluates its effectiveness across different deep learning architectures.

Findings

Synthetic fisheye images are effective for training deep learning models.

The approach improves free space detection accuracy.

Synthetic data can complement real datasets in autonomous driving.

Abstract

The development in the field of autonomous driving goes hand in hand with ever new developments in the field of image processing and machine learning methods. In order to fully exploit the advantages of deep learning, it is necessary to have sufficient labeled training data available. This is especially not the case for omnidirectional fisheye cameras. As a solution, we propose in this paper to use synthetic training data based on Unity3D. A five-pass algorithm is used to create a virtual fisheye camera. This synthetic training data is evaluated for the application of free space detection for different deep learning network architectures. The results indicate that synthetic fisheye images can be used in deep learning context.