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arXiv · 2504.14178

Segregation and Context Aggregation Network for Real-time Cloud Segmentation

Yijie Li·Hewei Wang·Jiayi Zhang·Jinjiang You·Jinfeng Xu·Puzhen Wu·Yunzhong Xiao·Soumyabrata Dev
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Abstract

Cloud segmentation from intensity images is a pivotal task in atmospheric science and computer vision, aiding weather forecasting and climate analysis. Ground-based sky/cloud segmentation extracts clouds from images for further feature analysis. Existing methods struggle to balance segmentation accuracy and computational efficiency, limiting real-world deployment on edge devices, so we introduce SCANet, a novel lightweight cloud segmentation model featuring Segregation and Context Aggregation Module (SCAM), which refines rough segmentation maps into weighted sky and cloud features processed separately. SCANet achieves state-of-the-art performance while drastically reducing computational complexity. SCANet-large (4.29M) achieves comparable accuracy to state-of-the-art methods with 70.9% fewer parameters. Meanwhile, SCANet-lite (90K) delivers 1390 fps in FP16, surpassing real-time standards. Additionally, we propose an efficient pre-training strategy that enhances performance even without ImageNet pre-training.

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Yijie Li, Hewei Wang, Jiayi Zhang, Jinjiang You, Jinfeng Xu, Puzhen Wu, Yunzhong Xiao, Soumyabrata Dev. 2025-04-19. Segregation and Context Aggregation Network for Real-time Cloud Segmentation. https://arxiv.org/abs/2504.14178

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