Wang Jianlong, Jia Junhao, Zhang Yake, Wang Haotian, Zhu Shisong
School of Computer Science and Technology, Henan Polytechnic University, Jiaozuo, China.
School of Computer and Information Engineering, Henan Normal University, Xinxiang, China.
Front Plant Sci. 2024 Mar 11;15:1305358. doi: 10.3389/fpls.2024.1305358. eCollection 2024.
Early detection of leaf diseases is necessary to control the spread of plant diseases, and one of the important steps is the segmentation of leaf and disease images. The uneven light and leaf overlap in complex situations make segmentation of leaves and diseases quite difficult. Moreover, the significant differences in ratios of leaf and disease pixels results in a challenge in identifying diseases.
To solve the above issues, the residual attention mechanism combined with atrous spatial pyramid pooling and weight compression loss of UNet is proposed, which is named RAAWC-UNet. Firstly, weights compression loss is a method that introduces a modulation factor in front of the cross-entropy loss, aiming at solving the problem of the imbalance between foreground and background pixels. Secondly, the residual network and the convolutional block attention module are combined to form Res_CBAM. It can accurately localize pixels at the edge of the disease and alleviate the vanishing of gradient and semantic information from downsampling. Finally, in the last layer of downsampling, the atrous spatial pyramid pooling is used instead of two convolutions to solve the problem of insufficient spatial context information.
The experimental results show that the proposed RAAWC-UNet increases the intersection over union in leaf and disease segmentation by 1.91% and 5.61%, and the pixel accuracy of disease by 4.65% compared with UNet.
The effectiveness of the proposed method was further verified by the better results in comparison with deep learning methods with similar network architectures.
早期检测叶片病害对于控制植物病害的传播至关重要，而叶片和病害图像的分割是重要步骤之一。复杂情况下光照不均和叶片重叠使得叶片和病害的分割颇具难度。此外，叶片和病害像素比例的显著差异给病害识别带来了挑战。
为解决上述问题，提出了结合空洞空间金字塔池化和UNet权重压缩损失的残差注意力机制，命名为RAAWC-UNet。首先，权重压缩损失是一种在交叉熵损失前引入调制因子的方法，旨在解决前景和背景像素不平衡的问题。其次，将残差网络和卷积块注意力模块相结合形成Res_CBAM。它能够准确地定位病害边缘的像素，并缓解下采样过程中梯度和语义信息的消失。最后，在最后一层下采样中，使用空洞空间金字塔池化代替两次卷积来解决空间上下文信息不足的问题。
实验结果表明，与UNet相比，所提出的RAAWC-UNet在叶片和病害分割中的交并比分别提高了1.91%和5.61%，病害的像素准确率提高了4.65%。
与具有相似网络架构的深度学习方法相比，更好的结果进一步验证了所提方法的有效性。
