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.
@article{arxiv.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}
}