Learning Collision-Free Space Detection from Stereo Images: Homography Matrix Brings Better Data Augmentation

TL;DR

This paper introduces a homography-based data augmentation method for stereo image-based collision-free space detection, significantly improving deep learning model performance with limited training data.

Contribution

It presents a novel homography matrix approach to generate augmented training data from multi-view images, enhancing neural network accuracy in space detection tasks.

Findings

Improved detection accuracy on multiple datasets.

Outperforms existing methods on the KITTI benchmark.

Effective with limited training samples.

Abstract

Collision-free space detection is a critical component of autonomous vehicle perception. The state-of-the-art algorithms are typically based on supervised learning. The performance of such approaches is always dependent on the quality and amount of labeled training data. Additionally, it remains an open challenge to train deep convolutional neural networks (DCNNs) using only a small quantity of training samples. Therefore, this paper mainly explores an effective training data augmentation approach that can be employed to improve the overall DCNN performance, when additional images captured from different views are available. Due to the fact that the pixels of the collision-free space (generally regarded as a planar surface) between two images captured from different views can be associated by a homography matrix, the scenario of the target image can be transformed into the reference view. This provides a simple but effective way of generating training data from additional multi-view images. Extensive experimental results, conducted with six state-of-the-art semantic segmentation DCNNs on three datasets, demonstrate the effectiveness of our proposed training data augmentation algorithm for enhancing collision-free space detection performance. When validated on the KITTI road benchmark, our approach provides the best results for stereo vision-based collision-free space detection.