# Benchmarking the Robustness of Semantic Segmentation Models

## TL;DR

This paper conducts an extensive benchmark of the robustness of semantic segmentation models, especially DeepLabv3+, against various image corruptions using nearly 400,000 images from multiple datasets.

## Contribution

It provides the first comprehensive robustness study for semantic segmentation models, revealing how architecture features influence resilience to corruptions.

## Key findings

- Robustness generally improves with model performance.
- Certain architectural components, like Dense Prediction Cells, impact robustness significantly.

## Abstract

When designing a semantic segmentation module for a practical application, such as autonomous driving, it is crucial to understand the robustness of the module with respect to a wide range of image corruptions. While there are recent robustness studies for full-image classification, we are the first to present an exhaustive study for semantic segmentation, based on the state-of-the-art model DeepLabv3+. To increase the realism of our study, we utilize almost 400,000 images generated from Cityscapes, PASCAL VOC 2012, and ADE20K. Based on the benchmark study, we gain several new insights. Firstly, contrary to full-image classification, model robustness increases with model performance, in most cases. Secondly, some architecture properties affect robustness significantly, such as a Dense Prediction Cell, which was designed to maximize performance on clean data only.