Analysis of the Over-Exposure Problem for Robust Scene Parsing

Developing a reliable high-level perception system that can work stably in different environments is highly useful, especially in autonomous driving tasks. Many previous studies have investigated extreme cases such as dark, rainy and foggy environments and proposed various datasets for these different tasks. In this work, we explore another extreme case: destructive over-exposure which may result in different degrees of content loss due to the limitations of dynamic range. These over-exposure cases can be found in most outdoor datasets with structured or unstructured environments but are usually neglected as they are mixed with other well-exposed images. To analyse the influence imposed by this kind of corruption, we generate realistic over-exposed images based on existing outdoor datasets using a simple but controllable formula proposed in a photographer's view. Our simulation is realistic, indicated by similar illumination distributions to other real over-exposed images. We also conduct several experiments on our over-exposed datasets and discover performance drops using state-of-the-art segmentation models. Subsequently, to address the over-exposure problem, we compare several image restoration approaches for over-exposure recovery and demonstrate their potential effectiveness as a preprocessing step in scene parsing tasks.