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Recent semantic segmentation models perform well under standard weatherconditions and sufficient illumination but struggle with adverse weatherconditions and nighttime. Collecting and annotating training data under theseconditions is expensive, time-consuming, error-prone, and not always practical.Usually, synthetic data is used as a feasible data source to increase theamount of training data. However, just directly using synthetic data mayactually harm the model's performance under normal weather conditions whilegetting only small gains in adverse situations. Therefore, we present a novelarchitecture specifically designed for using synthetic training data for domainadaptation. We propose a simple yet powerful addition to DeepLabV3+ by usingweather 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 athttps://github.com/lsmcolab/Semantic-Segmentation-under-Adverse-Conditions.

Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach