# Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective

arXiv: https://arxiv.org/abs/1809.04730 · HTML: https://mlsanity.com/paper/1809.04730/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Sep 2018.

## Built on by (most cited first, 1 of 1)

- [Bengali Document Layout Analysis -- A YOLOV8 Based Ensembling Approach](https://mlsanity.com/paper/2309.00848/) (2023)

## Abstract (arXiv)

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.