基于改进的VGG16网络金属表面缺陷图像分类研究
METAL SURFACE DEFECT IMAGE CLASSIFICATION BASED ON IMPROVED VGG16 NETWORK
胡坤 1吴国庆 1胡祖辉 2王忠明1
作者信息
- 1. 南通大学电气工程学院 江苏南通 226019
- 2. 南通大学信息科学技术学院 江苏南通 226019
- 折叠
摘要
针对工业生产中金属表面缺陷识别存在人力消耗大、效率不高等问题,提出改进的VGG16网络金属表面缺陷图像分类方法.以VGG16网络为基础,引入注意力机制CBAM增强特征学习能力,引入Inception网络结构拓宽网络宽度,以此增强模型非线性能力;对输入图像做数据增强处理,提高网络模型鲁棒性.实验证明,改进的网络模型在数据集GC10-DET上准确率达到90.23％,在数据集NEU-CLS上准确率达到98.84％.实验结果表明该方法在金属表面缺陷分类上具有良好的实际应用意义.
Abstract
Aimed at the problems of large labor consumption and low efficiency in metal surface defect recognition in industrial production,an improved VGG16 network metal surface defect image classification method is proposed.Based on the VGG16 network,the attention mechanism CBAM was introduced to enhance the feature learning ability,and the Inception network structure was introduced to broaden the network width,thereby enhancing the nonlinear ability of the model.The input image data was processed to improve the robustness of the network model.Through experimental verification,the improved network model has an accuracy rate of 90.23％on the data set GC10-DET,and an accuracy rate of 98.84％on the data set NEU-CLS.The experimental results show that this method has good practical application significance in the classification of metal surface defects.
关键词
缺陷图像分类/VGG16网络/注意力机制/Inception网络Key words
Defect image classification/VGG16 network/Attention mechanism/Inception network引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据