轻量级网络实现高精度云分割，兼顾速度与泛化能力。
DDUNet: Dual Dynamic U-Net for Highly-Efficient Cloud Segmentation
- 引入动态多尺度卷积与动态权重生成模块，增强特征融合与泛化。
- 仅用0.33M参数即达95.3%准确率，优于多种配置下的基准模型。
- 适合边缘部署，适用于实时云图像分析场景。
云分割旨在将图像中的云像素与非云像素分离。当前深度学习方法存在三方面问题：(a) 卷积核固定尺寸导致感受野受限；(b) 对不同场景鲁棒性不足；(c) 参数量大，难以实现实时应用。为此，本文提出双动态U-Net（DDUNet）用于监督式云分割。该网络采用U-Net架构，集成两个关键模块：动态多尺度卷积（DMSC），提升不同感受野下的特征融合能力；分类层中的动态权重与偏置生成器（DWBG），增强模型泛化性能。更重要的是，得益于深度可分离卷积设计，DDUNet为轻量级网络，在SWINySEG数据集上仅需0.33M参数即可达到95.3%的准确率，并在该数据集三种不同配置下均表现出更优的准确率与效率。
原文摘要 · Abstract (English)
Cloud segmentation amounts to separating cloud pixels from non-cloud pixels in an image. Current deep learning methods for cloud segmentation suffer from three issues. (a) Constrain on their receptive field due to the fixed size of the convolution kernel. (b) Lack of robustness towards different scenarios. (c) Requirement of a large number of parameters and limitations for real-time implementation. To address these issues, we propose a Dual Dynamic U-Net (DDUNet) for supervised cloud segmentation. The DDUNet adheres to a U-Net architecture and integrates two crucial modules: the dynamic multi-scale convolution (DMSC), improving merging features under different reception fields, and the dynamic weights and bias generator (DWBG) in classification layers to enhance generalization ability. More importantly, owing to the use of depth-wise convolution, the DDUNet is a lightweight network that can achieve 95.3% accuracy on the SWINySEG dataset with only 0.33M parameters, and achieve superior performance over three different configurations of the SWINySEg dataset in both accuracy and efficiency.
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