机械科学与技术2024，Vol.43Issue(6)：1042-1047,6.DOI:10.13433/j.cnki.1003-8728.20230010
改进DenseNet模型在工件表面粗糙度视觉检测中的应用
Application of Improved DenseNet Model to Visual Inspection of Workpiece Surface Roughness
摘要
Abstract
In order to solve the problem that the original DenseNet model with a long time and low accuracy to detect workpiece surface roughness,a deep learning model for workpiece surface roughness detection is proposed by combining the attention mechanism of convolutional layer filter and the scale coefficient of batch normalized layer.Firstly,the importance value of attention mechanism of convolution layer filter is used to determine the redundant channels in Dense Block module.Secondly,the scale coefficient of batch normalization layer was introduced into the Dense Block module of DenseNet model in order to distinguish the importance of feature channels.Finally,the attention importance value of convolution layer filter and scale coefficient of batch normalization layer are combined to crop redundant channels.Experimental results show that the accuracy of original DenseNet model is 91.875%,and the detection time is 483 s.When pruning rate is 20%,the detection accuracy was 96.875%,and detection time was 255 s.Comparing with the conventional model,the improved DenseNet model has better detection effect and larger application in the field of quality inspection.关键词
粗糙度检测/深度学习/DenseNet/模型剪枝Key words
roughness measurement/deep learning/DenseNet/model pruning分类
信息技术与安全科学引用本文复制引用
周友行,易倩,杨文佳,赵文杰..改进DenseNet模型在工件表面粗糙度视觉检测中的应用[J].机械科学与技术,2024,43(6):1042-1047,6.基金项目
国家自然科学基金项目(52175254,51775468)、湖南省教育厅科学研究项目(20A505)、湖南省研究生科研创新项目(CX20210645)及湘潭大学研究生科研创新项目(XDCX2021B174) （52175254,51775468）
