Rice is an important food crop, but it is susceptible to diseases during its growth process. Rapid, accurate, and effective identification of rice diseases is important for targeted measures to control disease spread. It is crucial for improving rice yield and quality. Therefore, this study proposes a CBAM-CARAFE-DeepLabv3+ rice disease segmentation method that combines attention mechanisms and feature recombination. This method focuses on three common diseases in rice growth: bacterial blight, blast, and brown spot disease. To enhance the extraction of favorable features, the algorithm adopts CBAM-RepViT as the backbone network. That is, the Squeeze-and-Excitation (SE) attention mechanism embedded in the efficient and lightweight RepViT network is replaced by the lightweight Convolutional Block Attention Module (CBAM). Compared to the SE attention mechanism, CBAM introduces a spatial attention module that focuses on important spatial positions in the feature map. It allows the model to extract more rich and detailed feature information by attending to both the channel and spatial dimensions of the feature map. Additionally, to further improve the feature extraction ability and image edge segmentation accuracy during upsampling, the lightweight Content-Aware ReAssembly of FEatures (CARAFE) operator is introduced into the decoding module for upsampling. Finally, to address the issue of imbalance between foreground and background pixel ratios in rice disease, a hybrid loss function composed of cross-entropy (CE) loss and Dice loss is proposed. Experimental results show that, compared to other networks such as DeepLabv3+, the proposed CBAM-CARAFE-DeepLabv3+ method achieves further improvement in segmentation accuracy, providing a new method for the development of rice disease segmentation technology.

中文翻译：

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基于CBAM-CARAFE-DeepLabv3+的水稻病害分割方法

水稻是重要的粮食作物，但在生长过程中容易受到病害的侵袭。快速、准确、有效地识别水稻病害对于采取针对性措施控制病害传播具有重要意义。对于提高水稻产量和品质至关重要。因此，本研究提出一种结合注意力机制和特征重组的CBAM-CARAFE-DeepLabv3+水稻病害分割方法。该方法主要针对水稻生长中的三种常见病害：白叶枯病、稻瘟病和褐斑病。为了增强有利特征的提取，算法采用CBAM-RepViT作为骨干网络。也就是说，高效轻量级 RepViT 网络中嵌入的挤压和激励（SE）注意力机制被轻量级卷积块注意力模块（CBAM）取代。与SE注意力机制相比，CBAM引入了空间注意力模块，该模块关注特征图中的重要空间位置。它允许模型通过关注特征图的通道和空间维度来提取更丰富和详细的特征信息。此外，为了进一步提高上采样时的特征提取能力和图像边缘分割精度，在解码模块中引入轻量级的内容感知特征重组（CARAFE）算子进行上采样。最后，为了解决水稻病害中前景和背景像素比例不平衡的问题，提出了一种由交叉熵（CE）损失和Dice损失组成的混合损失函数。实验结果表明，与DeepLabv3+等其他网络相比，所提出的CBAM-CARAFE-DeepLabv3+方法在分割精度上取得了进一步的提高，为水稻病害分割技术的发展提供了新的方法。