基于Tree-CNN的飞机腐蚀铆钉分类

Corroded Rivet Classification Based on Tree-CNN

针对目前飞机腐蚀铆钉分类准确率较低,且以手工检测为主的现状,提出一种基于Tree结构的CNN(Convolutional Neural Networks)分类算法用于飞机铆钉腐蚀分类。算法中Tree的深度和节点数由普通结构的CNN分类方法计算得到的铆钉类别的混淆矩阵决定,对于5分类的飞机铆钉实验,Tree的深度为3。经实验验证,所提出的Tree-CNN模型在飞机腐蚀铆钉数据集上分类精度达到86.5%,获得了较高的腐蚀铆钉分类准确率。

Considering that the accuracy of classification in corroded rivets is low and manual inspection is the main method,a Tree-CNN(Convolutional Neural Networks)classification method is proposed.This method is specially designed for classifying corroded rivets on aircrafts.In order to improve the classification accuracy of Tree-CNN method,the structure of the tree is determined by the confusion matrix of rivet categories which is calculated in normal CNN method.The depth of the tree is three for five-classification of corroded rivets.Experimental results show that by using the Tree-CNN method,the accuracy of classifying corroded rivets can reach up to 86.5%,which is effective in classification in corroded rivets.