Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach
Abdulrahman Kerim (Lancaster University),* Felipe C Chamone (Universidade Federal de Minas Gerais), Washington L. S. Ramos (Universidade Federal de Minas Gerais), Leandro Soriano Marcolino (Lancaster University), Erickson R. Nascimento (UFMG), Richard Jiang (Associate Professor, Lancaster University)
The 33rd British Machine Vision Conference
Abstract
Recent semantic segmentation models perform well under standard weather conditions and sufficient illumination but struggle with adverse weather conditions and nighttime. Collecting and annotating training data under these conditions is expensive, time-consuming, error-prone, and not always practical. Usually, synthetic data is used as a feasible data source to increase the amount of training data. However, just directly using synthetic data may actually harm the model’s performance under normal weather conditions while getting only small gains in adverse situations. Therefore, we present a novel architecture specifically designed for using synthetic training data for domain adaptation. We propose a simple yet powerful addition to DeepLabV3+ by using weather and time-of-the-day supervisors trained with multi-task learning, making it both weather and nighttime aware, which improves its mIoU accuracy by 14 percentage points on the ACDC dataset while maintaining a score of 75% mIoU on the Cityscapes dataset. Our code is available at https://github.com/lsmcolab/Semantic-Segmentation-under-Adverse-Conditions.Video
Citation
@inproceedings{Kerim_2022_BMVC,
author = {Abdulrahman Kerim and Felipe C Chamone and Washington L. S. Ramos and Leandro Soriano Marcolino and Erickson R. Nascimento and Richard Jiang},
title = {Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach},
booktitle = {33rd British Machine Vision Conference 2022, {BMVC} 2022, London, UK, November 21-24, 2022},
publisher = {{BMVA} Press},
year = {2022},
url = {https://bmvc2022.mpi-inf.mpg.de/0977.pdf}
}
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