基于改进全卷积网络的小麦图像分割

针对漫水填充结合模板匹配的双面联合分割方法对小麦图像进行分割存在过分割以及欠分割现象，提出基于改进的全卷积网络的图像语义分割方法.该方法融入前二个池化层的输出信息作为Softmax层的输入，探讨并得出了只融入第二个池化层的输出信息的网络模型优于同时融入前两个池化层的网络模型，引入Batch Normalization层到网络层中，并且针对小麦图像的需要将原来的21类网络输出类别更换为2类输出.实验采用建立的小麦图像数据库，结果表明改进后的网络使得过分割和欠分割现象明显减少，分割效果得到了显著提升，并且使用F-measure定量分析了模型的有效性.

Wheat Grain Image Segmentation Based on Improved Fully Convolutional Network

Because the result of method used by the project group currently which is a combination of floodfill and template matching is poor, and there is also under-segmentation or over-segmentation, this paper proposes the application of fully convolutional networks in semantic segmentation for wheat images. Firstly, the output information of the second pool layer is integrated as the input of the Softmax layer. Then, the Batch Normalization layer is introduced into the network layer, and 21 classes of output of the network are changed into the output of the 2 classes because of the characteristics of wheat. And the paper uses the F-measure to evaluate the result. The experimental results show that the proposed network can improve the segmentation result.