中圖分類號： TP183
文獻標識碼： A
DOI： 10.19358/j.issn.2096-5133.2020.06.006
引用格式： 鄭睿，余童，程龍閱. 基于改進LeNet-5的形狀類似物體識別方法[J].信息技術與網絡安全，2020，39(6)：31-37，43.

Recognition method of similar-shaped objects based on improved LeNet-5

Abstract： Aiming at the problems of low recognition accuracy and long time-consuming in the recognition of similar shape objects by deep learning, a recognition method based on improved LeNet-5 is proposed. Based on the traditional LeNet-5 network, changing the convolutional layer into a double-layer asymmetric convolution makes the network have better feature extraction capabilities; the generalization ability of the network is improved by batch normalization; the original Flatten layer is replaced by global average pooling，which is used to overcome the shortcomings of the traditional fully-connected layer with many parameters and long time-consuming; the training sample is increased by augmenting the training set. Experimental results show that the training accuracy of the improved LeNet-5 network reaches 91%, the accuracy of identifying objects with similar shapes is 87%, and it can converge within a small number of iterations. These indicators are significantly better than the original network.

Key words : LeNet-5 network；image recognition；asymmetric convolution；batch normalization；maximum average pooling