[1]张素智,吴玉红,常 俊.基于改进 AlexNet 卷积神经网络的轮胎图像识别[J].计算机技术与发展,2021,31(07):182-186.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 030]
　ZHANG Su-zhi,WU Yu-hong,CHANG Jun.Tire Damage Image Recognition Based on Improved AlexNetConvolutional Neural Network[J].,2021,31(07):182-186.[doi:10. 3969 / j. issn. 1673-629X. 2021. 07. 030]
点击复制

基于改进 AlexNet 卷积神经网络的轮胎图像识别()
分享到：

《计算机技术与发展》[ISSN:1006-6977/CN:61-1281/TN]

卷:
31
期数:
2021年07期
页码:
182-186
栏目:
应用前沿与综合
出版日期:
2021-07-10

文章信息/Info

Title:
Tire Damage Image Recognition Based on Improved AlexNetConvolutional Neural Network
文章编号:
1673-629X(2021)07-0182-05
作者:
张素智1; 吴玉红2; 常 俊2
1. 郑州轻工业大学 软件学院,河南 郑州 450000;
2. 郑州轻工业大学 计算机与通信工程学院,河南 郑州 450000
Author(s):
ZHANG Su-zhi1; WU Yu-hong2; CHANG Jun2
1. School of Software,Zhengzhou University of Light Industry,Zhengzhou 450000,China;
2. School of Computer and Communication Engineering,Zhengzhou University of Light Industry,Zhengzhou 450000,China
关键词:
图像识别; 特征信息; 卷积神经网络; 回归模型; 损失函数
Keywords:
image recognition; feature information; convolutional neural network; regression model; loss function
分类号:
TP31
DOI:
10. 3969 / j. issn. 1673-629X. 2021. 07. 030
摘要:
在轮胎破损图像识别的应用中,为了提高图像识别的准确率,需要提取轮胎图像的特征信息,通过利用卷积神经网络对数据集进行反复训练,然后得到轮胎破损图像的训练模型。 而卷积神经网络在轮胎破损图像识别应用中容易出现过拟合现象,使得图像的训练准确率降低。 针对此问题,提出了一种基于 AlexNet 卷积神经网络与岭回归分析相结合的图像识别算法。 通过对岭回归分析和 AlexNet 卷积神经网络识别算法进行的学习研究,在算法原来的损失函数中引入一个新的正则项,以此改变新的损失函数中两部分的占比关系,降低特征信息的拟合曲线抖动的幅度。 最终通过实验验证了改进的 AlexNet 卷积神经网络可以提高轮胎破损图像的训练准确率和识别率。
Abstract:
In the application of tire damage image recognition,in order to improve the accuracy of image recognition,it is necessary to extract the feature information of the tire image. The convolutional neural network is used to repeatedly train the data set and then obtain the training model of the tire damage image. The convolutional neural network is prone to over fitting in the application of tire damage image recognition, which reduces the accuracy of image training. To solve this problem,an image recognition algorithm based on the combination of AlexNet convolutional neural network and ridge regression analysis is proposed. Through the study of ridge regression analysis and AlexNet convolutional neural network recognition algorithm,a new regular term is introduced into the original loss function of the algorithm,so as to change the ratio of the two parts in the new loss function and reduce the magnitude of the jitter of the fitted curve in feature information. Finally,experiments have also verified that the improved AlexNet convolutional neural network can improve the training accuracy and recognition rate of tire damaged images.

相似文献/References:

[1]王明平 宋丽梅.基于计算机视觉的车架号采集系统[J].计算机技术与发展,2008,(04):239.
　WANG Ming-ping,SONG Li-mei.Vehicle Identify Number Acquisition System Based on Machine-Vision[J].,2008,(07):239.
[2]徐仕玲 赵敏 徐建波.野外早期火灾图像识别方法研究[J].计算机技术与发展,2008,(06):214.
　XU Shi-ling,ZHAO Min,XU Jian-bo.Research for Early Fire Image Recognition Technology in Outdoors[J].,2008,(07):214.
[3]张婷 吴元君 黄俊 吴建国.选票选举系统中选票图像的预处理方法研究[J].计算机技术与发展,2007,(04):225.
　ZHANG Ting,WU Yuan-jun,HUANG Jun,et al.Research of Image Pre- Processing in Vote Processing System[J].,2007,(07):225.
[4]吴忠 朱国龙 黄葛峰 吴建国.基于图像识别技术的手写数字识别方法[J].计算机技术与发展,2011,(12):48.
　WU Zhong,ZHU Guo-long,HUANG Ge-feng,et al.Handwritten Digit Recognition Based on Image Recognition System[J].,2011,(07):48.
[5]汪晨 张涛 林为民 邓松 时坚 李伟伟.图像识别综述及在电力信息安全中的应用研究[J].计算机技术与发展,2012,(04):161.
　WANG Chen,ZHANG Tao,LIN Wei-min,et al.Image Recognition Review and Application Research in Electric Power Information Security[J].,2012,(07):161.
[6]王珂雅 邱力军.一种新的脑部CT图像异常检测算法[J].计算机技术与发展,2012,(05):185.
　WANG Ke-ya,QIU Li-jun.A New Anomaly Detection Algorithm for Brain CT Image[J].,2012,(07):185.
[7]周诚诚 张代远[].利用图像识别技术过滤海量可疑钓鱼网站[J].计算机技术与发展,2012,(11):246.
　ZHOU Cheng-cheng,ZHANG Dai-yuan.Using Image Recognition Technology to Filter Mass Suspicious Phishing Sites[J].,2012,(07):246.
[8]毕 杨,宋 飞,王 轩.一种航空维修工具智能管理系统的设计与实现[J].计算机技术与发展,2020,30(07):199.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 041]
　BI Yang,SONG Fei,WANG Xuan.Design and Implementation of an Intelligent Management System of Aviation Maintenance Tools[J].,2020,30(07):199.[doi:10. 3969 / j. issn. 1673-629X. 2020. 07. 041]
[9]严一鸣[],郭星[]. 基于计算机视觉的交互式电子沙盘系统研究[J].计算机技术与发展,2017,27(06):195.
　YAN Yi-ming[],GUO Xing[]. Investigation on Interactive Electronic Sand Table System with Computer Vision[J].,2017,27(07):195.
[10]高友文,周本君,胡晓飞.基于数据增强的卷积神经网络图像识别研究[J].计算机技术与发展,2018,28(08):62.[doi:10.3969/ j. issn.1673-629X.2018.08.013]
　GAO You-wen,ZHOU Ben-jun,HU Xiao-fei.Research on Image Recognition of Convolution Neural Network Based on Data Enhancement[J].,2018,28(07):62.[doi:10.3969/ j. issn.1673-629X.2018.08.013]

更新日期/Last Update: 2021-07-10