# Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective Source: [Adapting Semantic Segmentation Models for Changes in Illumination and Camera Perspective]() Collection index: [llms.txt]() Data updated: 2025-10-28T13:56:38.576Z Authors: Wei Zhou, Alex Zyner, Stewart Worrall, Eduardo Nebot Published: 2018-09-13T01:17:54.000Z ## 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()