基于改进全卷积神经网络的玉米叶片病斑分割

玉米叶部病斑的准确分割是识别玉米叶部病害类别、实现作物精准施药的关键。为了准确分割出玉米叶部的病斑区域，提出了一种基于改进全卷积神经网络（Fully Convolutional Neural Network，FCN）的玉米叶片病斑分割方法。该方法的网络结构主要包括一个编码网络和对应的解码网络，在解码网络之后添加一个像素级别的分类器。编码网络结构是在传统VGG16网络的基础上进行改进，解码网络主要是对编码网络中的下采样层进行反卷积操作，通过对解码网络不断地进行训练，可以恢复编码网络输出特征图的分辨率，得到更为精确的分割效果。利用该研究方法与FCN、DeepLabV3、PSP Net等图像分割网络模型在不同的评价指标上进行比较，结果表明研究方法具有较好的分割性能，可以准确分割出玉米叶部的病斑区域。

Segmentation of Corn Leaf Diseases Based on Improved Fully Convolutional Neural Network

Accurate segmentation of corn leaf diseases is the key of recognizing the types of corn leaf diseases and achieving the precise application of crop medicine. In order to segment the diseased area of corn leaf accurately, this paper proposes a method for the segmentation of corn leaf diseases based on improved Fully Convolutional Neural Network（FCN）. The network structure of the proposed method mainly includes an encoding network and corresponding decoding network, and a pixel-level classifier is added behind the decoding network. The structure of the encoding network is improved on the basis of the traditional VGG-16 network. The decoding network is mainly used for the deconvolution operation of the lower sampling layer in the encoding network. By training the decoding network continuously, the resolution of the output characteristic graph of the encoding network can be recovered, and more accurate segmentation effect can be obtained. The results show that this method has good segmentation performance and can accurately segment the diseased spot area of corn leaf, compared with FCN, DeepLabV3, PSP Net and other image segmentation network models on different evaluation indexes.