摘要
对轻量级卷积神经网络MobileNet V2的模型结构进行改进,将深度可分离卷积中的激活函数ReLU替换成Leaky ReLU,从而避免神经元死亡问题,倒置残差卷积中的跨越连接添加Dropout层,增大模型的泛化能力。实验结果表明,预测结果的总体准确率达到91.41%,最高精确率为95.12%,最高召回率为97.39%,取得较好的预测结果。这说明将MobileNet V2卷积神经网络用于植物叶片图像识别是实际可行的,为移动端植物叶片图像识别提供了实现方法和技术支撑。
This paper proposes to improve the model structure of the lightweight convolutional neural network MobileNet V2.The activation function ReLU in the deep separable convolution is replaced with Leaky ReLU,thereby avoiding the problem of neuron death.A Dropout layer is added across connections in inverted residual convolution to increase the generalization of the model.The experimental results show that the overall accuracy rate of the prediction results reaches 91.41%,the highest accuracy rate is 95.12%,and the highest recall rate is 97.39%,achieving good prediction results.It shows that it is practical to use the MobileNet V2 convolutional neural network for plant leaf image recognition,and it provides an implementation method and technical support for mobile terminal realization of plant leaf image recognition.
作者
李文逵
韩俊英
LI Wenkui;HAN Junying(College of Information Science and Technology,Gansu Agricultural University,Lanzhou 730070,China)
出处
《软件工程》
2022年第2期10-13,9,共5页
Software Engineering
基金
甘肃省自然科学基金资助项目(20JR5RA023).
