Abstract: With the rapid development of aviation fastener manufacturing,the fastener manufacturing process on the production workshop assembly linebecomes more and more complicated.At present,the transfer of different fastener products in the production line still stays in the manual work.This method is not only complicated and tiring,but also difficult to satisfy the real-time classification requirement.In this paper,an automatic classification method for aviation fasteners based on image classification algorithms is proposed.A set of fastener image acquisition and automatic classification implementation schemes are designed,and evaluation experiments are performed based on real industrial data.The evaluation experiments count convolutional neural networks (CNN) and Inception-v3 model accuracy,recall,precision and F1-Score.The experimental results show that Inception-v3 is superior to CNN in various evaluation indicators,and the accuracy of Inception-v3 model classification reaches more than 98%,which can effectively realize automatic classification of aviation fastener products.

Key words: Aviation fastener, Convolutional neural network, Image classification, Inception-v3