基于Tree-CNN 的飞机腐蚀铆钉分类

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。

Corroded Ｒivet Classification Based on Tree-CNN

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．