改进残差网络甜瓜叶片病害的识别研究OA北大核心CSTPCD
Research on Identification of Melon Leaf Diseases with Improved Residual Network
针对甜瓜叶片不同程度的病害识别研究较少,人工检测实时性差且存在识别准确率较低等问题,提出了一种基于改进残差网络模型的甜瓜叶片病害识别方法.将传统的ResNet50模型作为骨干网络,将ReLU激活函数替换为ELU激活函数;将ResNet50的模型的第一层卷积中的7×7卷积核替换成Incption结构,在全连接层之后加入Dropout层,增强模型的表达能力并缓解过拟合问题;引入多头自注意力机制(MHSA),提高模型的泛化能力.进行数据预处理,将训练集…查看全部>>
In view of the fact that there are few studies on the identification of diseases of muskmelon leaves in different degrees,the manual detection has poor real-time performance and the identification accuracy is low,a method for identifi-cation of muskmelon leaf diseases based on the improved residual network model is proposed.The traditional ResNet50 model is used as the backbone network,and the ReLU activation function is replaced by the ELU activation functi…查看全部>>
黄英来;姜忠良
东北林业大学 计算机与控制工程学院,哈尔滨 150040东北林业大学 计算机与控制工程学院,哈尔滨 150040
计算机与自动化
甜瓜叶片病害图像识别残差网络多头自注意力机制深度学习
melon leaf diseasesimage recognitionresidual networkmulti-head self-attention(MHSA)mechanismdeep learning
《计算机工程与应用》 2024 (15)
189-197,9
国家自然科学基金(31670717)黑龙江省自然科学基金(LH2020C051).
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