【摘要】 针对SENet的通道注意力机制特征提取单一和分割的幼苗数据集图片存在部分缺失的难点问题，设计了一种基于双通道注意力机制的残差网络。该网络融合通道注意力机制和空间注意力机制模块，可同时获得通道和空间维度特征权重，提升网络的特征学习能力。提出了一种随机擦除方法，来解决分割样本数据中目标部分缺失的难点问题。在自制的穴盘幼苗Plant＿seed数据集上的实验结果表明，在ResNet34残差模块和conv~*＿x模块之间均引入注意力机制模块的改进网络ResNet34+CBAM＿basic＿conv的准确率最优，达到93.8%，同时对数据集部分图片进行随机擦除后，模型分类的错误率下降，验证了所提方法的优异性能。更多还原

【Abstract】 A residual network based on dual-channel attention mechanism is designed to address the difficulty in extracting single and segmented seedling dataset images from the channel attention mechanism feature of the SENet network, which integrates the channel attention mechanism and spatial attention. The mechanism module can obtain the channel and spatial dimension feature weights simultaneously to enhance the feature learning ability of the network. To address the problem of missing the target in the segmented sample data, a random erasure method is proposed. Experiments on the self-made plug seedling Plant＿seed dataset demonstrate that the improved network ResNet34+CBAM＿basic＿conv, which introduces the attention mechanism module between the ResNet34 network residual module and the conv~*＿x module,reaches the optimal accuracy of 93. 8%. The error rate of the model classification drops after some images in the dataset are randomly erased, demonstrating the excellent performance of the proposed method.更多还原