Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective

TL;DR

This paper proposes a data augmentation approach using skew and gamma correction to improve the robustness of semantic segmentation models against changes in illumination and camera perspective, crucial for autonomous vehicle perception.

Contribution

It introduces a practical data augmentation method to adapt existing semantic segmentation models for varying lighting and camera angles without extensive retraining.

Findings

Significant performance improvements under different illumination conditions.

Enhanced model robustness to camera perspective changes.

Effective augmentation techniques for real-world autonomous driving scenarios.

Abstract

Semantic segmentation using deep neural networks has been widely explored to generate high-level contextual information for autonomous vehicles. To acquire a complete $180^\circ$ semantic understanding of the forward surroundings, we propose to stitch semantic images from multiple cameras with varying orientations. However, previously trained semantic segmentation models showed unacceptable performance after significant changes to the camera orientations and the lighting conditions. To avoid time-consuming hand labeling, we explore and evaluate the use of data augmentation techniques, specifically skew and gamma correction, from a practical real-world standpoint to extend the existing model and provide more robust performance. The presented experimental results have shown significant improvements with varying illumination and camera perspective changes.