基于Mask R-CNN的复合材料夹杂缺陷自动检测研究

Research on automatic detection of composite inclusion defects based on Mask R-CNN

为提高复合材料夹杂缺陷的检测效率,本文提出利用深度学习网络设计一种夹杂缺陷自动检测系统。在图像预处理环节采用两级反锐化掩膜算法突出夹杂缺陷特征,构建复合材料夹杂缺陷图像数据库;采用Mask R-CNN网络模型,经过网络模型训练,得到最优权重参数,最终设计实现缺陷检测软件系统。实验结果表明,Mask R-CNN算法网络准确率达94.6%,召回率达92.4%,AP值达87.3%。该系统应用方便快捷,将有效提高一线人员的缺陷检测效率和检测精度。

In order to improve the detection efficiency of composite inclusion defects,an automatic inclusion defect detection system based on deep learning network is proposed in this paper.In the process of image preprocessing,a two-stage unsharping mask algorithm is used to highlight the features of inclusion defects,and a composite image database of inclusion defects is constructed.The Mask R-CNN network model is used,and the optimal weight parameters are obtained through network model training.Finally,the defect detection software system is designed and realized.The experimental results show that the network accuracy of Mask R-CNN algorithm is 94.6%,the recall rate is 92.4%,and the AP value is 87.3%.The system is convenient and fast in application,and will effectively improve the efficiency and accuracy of defect detection for front-line personnel.