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Semantic segmentation plays a crucial role in the fields of computer vision and computer graphics, with extensive applications in various practical scenarios. Significant progress has been made in semantic segmentation tasks using deep learning-based methods. However, most existing semantic segmentation algorithms focus on good weather conditions, and they face challenges in terms of accuracy and robustness when applied to rainy scenes due to factors such as raindrops, haze, and lighting variations. To address this issue, this paper proposes a rainy-day semantic segmentation method based on a two-stage progressive network. The proposed method consists of two modules: a rain removal module responsible for eliminating raindrops and haze from the input rainy images and restoring the basic structural information of the images, and a segmentation module that performs pixel-level semantic prediction on the rain-removed images. Specifically, the rain removal module introduces two progressive units with shared weights to gradually achieve rain removal. The segmentation module adopts an encoder–decoder architecture, utilizing down-sampling and deep asynchronous bottleneck units for encoding. It also introduces a dual attention-guided fusion module to aggregate channel attention information and spatial attention information, guiding the multiscale feature fusion process in the decoder. Experimental results demonstrate that this method effectively mitigates the influence of rain streaks on semantic segmentation, thereby improving segmentation performance and achieving more accurate and robust semantic segmentation results in rainy conditions. We will provide the code and datasets on https://github.com/zhang152267/TSPN. [ABSTRACT FROM AUTHOR]