摘要
针对轮胎激光散斑图识别精度低的问题,本文提出了一种新的轮胎激光散斑图分类网络(CA-ResNet50)。首先选用ResNet50为基础的残差网络,改变传统ResNet50网络模型中的残差块结构,最大程度发挥批标准化的作用;再引入轻量级的卷积注意力模块,增强网络模型对轮胎缺陷的特征提取能力;然后,用LeakyRelu激活函数代替Relu激活函数,解决神经元的“失活”问题;最后,对训练数据集进行扩展,以克服训练中数据量不足和网络模型拟合过度的问题。将本文中提出的CA-ResNet50与当前常用的分类网络模型在相同的数据集上进行对比,实验结果证明本文所提网络模型对轮胎激光散斑图的测试精度高于其他网络,识别精度可达到99.7%。
To address the problem of low accuracy of tire laser scattergram recognition,this paper proposes a new classification network for tire laser scattergram(CA-ResNet50).Firstly,ResNet50-based residual network is selected to change the residual block structure in the traditional ResNet50network model to maximize the role of batch normalization.Then,a lightweight convolutional attention module is introduced to enhance the feature extraction ability of the network model for tire defects.Next,LeakyRelu activation function is used instead of the Relu activation function to solve the neuronal deactivation problems.Finally,the training data set is extended to overcome the problems of insufficient data volume and overfitting of the network model in training.The CA-ResNet50proposed in this paper is compared with the current commonly used classification network models on the same dataset,and the experimental results prove that the testing accuracy of the proposed network model in this paper is higher than other networks for tire laser scatter maps,and the recognition accuracy can reach 99.7%.
作者
刘韵婷
葛忠文
郭辉
Liu Yunting;Ge Zhongwen;Guo Hui(Shenyang Ligong University,Shenyang 110000,China;Sports Equipment Industry Technology Research Institute,Shenyang University of Technilogy,Shenyang 110870,China)
出处
《电子测量技术》
北大核心
2023年第4期169-174,共6页
Electronic Measurement Technology
基金
国家重点研发计划(19YJC890012)
辽宁省教育厅项目(LJGD2020019)
国家重点研发计划(2017YFC082100-2)项目资助
关键词
轮胎激光散斑图
图像识别
迁移学习
残差网络
tyre laser scattergrams
image recognition
migration learning
residual networks
