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.
View on arXiv@article{li2025_2504.14178,
title={ Segregation and Context Aggregation Network for Real-time Cloud Segmentation },
author={ Yijie Li and Hewei Wang and Jiayi Zhang and Jinjiang You and Jinfeng Xu and Puzhen Wu and Yunzhong Xiao and Soumyabrata Dev },
journal={arXiv preprint arXiv:2504.14178},
year={ 2025 }
}