基于注意力机制的改进UNet草莓病害语义分割
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Improved UNet Based on Attention Mechanism in Semantic Segmentation of Strawberry Diseases
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    摘要:

    针对当前传统农作物病害语义分割方法精度不高、鲁棒性差等问题, 本文提出了基于注意力机制的改进UNet草莓病害语义分割模型. 首先, 在编码器中加入CNN-Transformer混合结构, 增强全局信息与局部细节信息的特征提取能力. 其次, 在解码器中将dual up-sample模块替换传统上采样, 提高特征提取能力与分割精度. 再使用hard-swish激活函数代替ReLU激活函数, 更加平滑的曲线有助于提高泛化性和非线性特征提取能力, 防止梯度消失. 最后, 通过使用结合交叉熵Dice损失函数, 加强模型对分割结果的约束, 进一步提升分割精度. 实验采用了由7种草莓病害2500张图像组成的数据集, 在复杂背景下对草莓病害进行分割, 语义分割像素精度达到92.56%, 平均交并比达到84.97%. 实验结果表明, 本文的改进UNet在草莓病害语义分割方面, 能实现更好的分割效果, 优于大多数分割模型.

    Abstract:

    The existing traditional semantic segmentation methods of crop diseases have low accuracy and poor robustness. In order to address these problems, an improved UNet semantic segmentation model of strawberry diseases based on an attention mechanism is proposed. Firstly, a CNN-Transformer hybrid structure is added to the encoder to improve the feature extraction ability of global information and local detail information. Secondly, the traditional up-sampling is replaced by a dual up-sample module in the decoder to enhance the feature extraction ability and segmentation accuracy. Thirdly, the hard-swish activation function is employed to replace the ReLU activation function, and the smoother curve helps to improve generalization and nonlinear feature extraction ability and prevent gradient disappearance. Finally, the segmentation accuracy is further improved by using a combined cross-entropy Dice loss function to strengthen the model’s constraints on the segmentation results. A dataset consisting of 2 500 images of seven strawberry diseases is used to segment strawberry diseases in a complex background. The semantic segmentation pixel accuracy reaches 92.56%, and the average cross-merge ratio reaches 84.97%. The experimental results show that the improved UNet in this study can achieve better segmentation results and outperform most segmentation models in the semantic segmentation of strawberry diseases.

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毛万菁,阮炬全,刘朔.基于注意力机制的改进UNet草莓病害语义分割.计算机系统应用,2023,32(6):251-259

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  • 收稿日期:2022-11-10
  • 最后修改日期:2023-02-03
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  • 在线发布日期: 2023-04-23
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