计算机应用与软件2024，Vol.41Issue(6)：175-180,6.DOI:10.3969/j.issn.1000-386x.2024.06.026
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
摘要
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分类
信息技术与安全科学引用本文复制引用
胡坤,吴国庆,胡祖辉,王忠明..基于改进的VGG16网络金属表面缺陷图像分类研究[J].计算机应用与软件,2024,41(6):175-180,6.基金项目
国家自然科学基金项目(61273151). （61273151）
