2.4.1. Inception Structure

Inception structure is a significant breakthrough in the development history of CNN models. Its purpose is to execute multiple convolution operations or pooling operations on the input image in parallel and concatenate all the outputs to attain more comprehensive image features. This structure was first introduced by GoogLeNet and called Inceptionv1 [28]. Subsequently, it was improved to the Inception-v2 structure by applying batch normalization (BN) [29] and convolutional decomposition. Then, it evolved into the Inception-v3 network by adding asymmetric convolution, auxiliary classifiers, etc. The architecture not only accelerates the computation but also improves the generalization ability of the model while eliminating the use of dropout in the batch normalization network [30]. Currently, the Inception structure has been developed to the Inception-v4 [31].
