摘要
为了能够对木节缺陷进行准确识别,减少木材的浪费,本研究在Pytorch深度学习框架的基础上,提出一种基于GoogLeNet卷积神经网络的木节缺陷识别方法。该方法利用GoogLeNet网络对朽节、干节和死节等7种云杉木节缺陷的RGB图像进行自动提取特征,不需要对图像进行预处理,即可实现分类识别,采用全局平均池化的方法来代替全连接层,减少网络的参量。同时为了防止网络的过拟合,在网络中使用Dropout机制。实验结果表明,利用该卷积神经网络对7种木节缺陷的识别率可以达到95.42%,在木节缺陷图像处理中,GoogLeNet模型能准确有效地识别木节缺陷。
In order to recognize the knots accurately and reduce the waste of wood,a method of recognizing the knots based on GoogLeNet convolution neural network based on the deep learning framework of Pytorch was proposed in this paper.This method used GoogLeNet network to extract features automatically from RGB images of seven kinds of spruce knots,such as decayed knots,dry knots and encased knots et al,so that classification and recognition can be realized without image preprocessing.Global average pooling was used to replace the fully connected layer,and the network parameters can be reduced.At the same time,in order to prevent over-fitting,Dropout mechanism was used in the network.The experimental results showed that,the recognition accuracy of seven kinds of knot defects using the convolutional neural network can reach 95.42%,and the GoogLeNet can identify the knots accurately and effectively in the image processing of wood knot defects.
作者
高明宇
倪海明
张博洋
陈剑峰
戚大伟
牟洪波
GAO Mingyu;NI Haiming;ZHANG Boyang;CHEN Jianfeng;QI Dawei;MU Hongbo(College of Science,Northeast Forestry University,Harbin 150040,China)
出处
《森林工程》
北大核心
2021年第4期66-70,共5页
Forest Engineering
基金
中央高校基本科研业务费专项资金资助项目(2572020BC07)
国家自然科学基金项目(31570712)。
