论文标题
语义细分在不利条件下：天气和夜间感知的基于合成数据的方法
Semantic Segmentation under Adverse Conditions: A Weather and Nighttime-aware Synthetic Data-based Approach
论文作者
论文摘要
最近的语义细分模型在标准天气条件下表现良好，有足够的照明，但在不利的天气条件和夜间中挣扎。在这些条件下收集和注释培训数据是昂贵的，耗时的，容易出错的，并且并非总是实用的。通常，合成数据用作可行的数据源，以增加培训数据的量。但是，只有直接使用合成数据实际上可能会在正常天气条件下损害模型的性能，同时在不利的情况下仅获得很小的收益。因此，我们提出了一种专门设计用于域适应性合成训练数据的新型体系结构。我们通过使用经过多任务学习培训的天气和日期主管，提出了一个简单而有力的补充，使其既有多任务学习，又使其既有天气和夜间意识，又使其MIOU准确度提高了ACDC数据集中的$ 14 $百分比点，同时保持了$ 75 \％$ $ MIOU的$ 75 \％$ MIOU的CityScapes vataSet。我们的代码可在https://github.com/lsmcolab/semantic-semantic-sementation-under-fordserver-conditions上找到。
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.
