On the Impact of Illumination-Invariant Image Pre-transformation on Contemporary Automotive Semantic Scene Understanding

Abstract

Illumination changes in outdoor environments under non-ideal weather conditions have a negative impact on automotive scene understanding and segmentation performance. In this paper, we present an evaluation of illuminationinvariant image transforms applied to this application domain. We compare four recent transforms for illumination invariant image representation, individually and with colour hybrid images, to show that despite assumptions to contrary such invariant pre-processing can improve the state of the art in scene understanding performance. In addition, we propose a robust approach based on using an illumination-invariant image representation, combined with the chromatic component of a perceptual colour-space to improve contemporary automotive scene understanding and segmentation. By using an illumination invariant pre-process, to reduce the impact of environmental illumination changes, we show that the performance of deep convolutional neural network based scene understanding and segmentation can yet be further improved. This illuminating result enforces the need for invariant (unbiased) training sets within such deep network training and shows that even a welltrained network may still not offer truly optimal performance (if we ignore any prior data transforms attributable to a priori insight). Our approach is demonstrated over a range of example imagery where we show a notable improvement in performance using pre-processed, illumination invariant, automotive scene imagery.