CloudSwinNetLite： LIGHTWEIGHT FINE-GRAINED SEGMENTATION NETWORK FOR GROUND-BASED CLOUD IMAGE

Shi Chaojun; Xie Xiongbin; Zhang Ke; Su Zibo; Zhang Xiaoyun; Li Xingkuan

doi:10.19912/j.0254-0096.tynxb.2024-2425

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (11) : 374-382. DOI: 10.19912/j.0254-0096.tynxb.2024-2425

CloudSwinNetLite： LIGHTWEIGHT FINE-GRAINED SEGMENTATION NETWORK FOR GROUND-BASED CLOUD IMAGE

Shi Chaojun^1,2, Xie Xiongbin¹, Zhang Ke^1,2, Su Zibo¹, Zhang Xiaoyun¹, Li Xingkuan¹

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Abstract

In existing cloud image segmentation research, most methods only distinguish between the sky and clouds, neglecting the inherent differences among various cloud types. In addition, many current models are computationally intensive and inefficient, making them unsuitable for the real-time segmentation of cloud images and thus limiting their application in ultra-short-term photovoltaic power forecasting. To overcome these challenges, we propose CloudSwinNetLite, a lightweight fine-grained cloud image segmentation network. The model adopts an encoder-decoder structure with Swin Transformer as the base module, using a lightweight multi-head self-attention mechanism （W-GLWMSA） and a ghost-based inverted residual feed-forward network （GIRFFN）. These designs jointly enhance computational efficiency while preserving high segmentation accuracy. Experimental results demonstrate that CloudSwinNetLite achieves effective fine-grained cloud segmentation with substantially reduced computational cost compared with existing models.

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ground-based cloud image / photovoltaic power / forecasting / fine-grained segmentation / lightweight net

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Shi Chaojun, Xie Xiongbin, Zhang Ke, Su Zibo, Zhang Xiaoyun, Li Xingkuan. CloudSwinNetLite： LIGHTWEIGHT FINE-GRAINED SEGMENTATION NETWORK FOR GROUND-BASED CLOUD IMAGE[J]. Acta Energiae Solaris Sinica. 2025, 46(11): 374-382 https://doi.org/10.19912/j.0254-0096.tynxb.2024-2425

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