Fine-Grained Segmentation Method of Ground-Based Cloud Images Based on Improved Transformer

Solar irradiance is one of the main factors affecting the output of photovoltaic power stations. The cloud distribution above the photovoltaic power station can determine the strength of the absorbed solar irradiance. Cloud estimation is another important factor affecting the output of photovoltaic power stations. Ground-based cloud automation observation is an important means to achieve cloud estimation and cloud distribution. Ground-based cloud image segmentation is an important component of ground-based cloud image automation observation. Most of the previous ground-based cloud image segmentation methods rely on convolutional neural networks (CNNs) and lack modeling of long-distance dependencies. In view of the rich fine-grained attributes in ground-based cloud images, this paper proposes a new Transformer architecture for ground-based cloud image fine-grained segmentation based on deep learning technology. The model consists of an encoder–decoder. In order to further mine the fine-grained features of the image, the BiFormer Block is used to replace the original Transformer; in order to reduce the model parameters, the MLP is used to replace the original bottleneck layer; and for the local features of the ground-based cloud, a multi-scale dual-attention (MSDA) block is used to integrate in the jump connection, so that the model can further extract local features and global features. The model is analyzed from both quantitative and qualitative aspects. Our model achieves the best segmentation accuracy, with mIoU reaching 65.18%. The ablation experiment results prove the contribution of key components to segmentation accuracy.