## 1 Introduction  
Deep learning [1] is a field of machine learning research. Its purpose is to train computers to complete human behaviors such as autonomous learning, judgment and decision making. It imitates the mechanisms of the human brain to explain data such as images, sounds and text [2]. With the development of the Internet and computer hardware, deep learning has an increasingly wide range of applications in image recognition [3]. In recent years, it has shown amazing capabilities. Image recognition [4] is a technology that uses computers to analyze and process images to identify targets and objects in various patterns, based on the main features of the image. It is based on the main features of the image. There are many algorithms for image recognition, such as the improved differential evolution algorithm [5, 6], enhanced MSI-QDE algorithm [7], neural network algorithm, and so on. Among them, the convolutional neural network [8] is one of the representative algorithms for image recognition using a neural network. The earliest emerging convolutional neural network algorithms are the Time Delay Neural Network [9] and LeNet-5 [10]. In 2012, AlexNet [11], which was designed by [PERSON] and his student [PERSON], won the ImageNet competition and attracted widespread attention. In the ILSVRC competition in 2013, [PERSON] and others improved AlexNet and innovatively added the convolution visualization function to form ZFNet [12], which won first place in the image classification task. At first, neural networks were mainly studied in terms of increasing depth, such as the top two GoogLeNet [13]and VGG [14] in the 2014 ILSVRC competition. VGG deepens the network depth to 19 layers and GoogLeNet to 22 layers. These have achieved good results. As the depth increases, the performance of the convolutional neural network structure becomes stronger and stronger. The problems of over-fitting, the disappearance of gradients, the huge amount of model parameters and difficulty in optimization are also becoming more and more prominent. Researchers have proposed different deep convolutional neural network model structures for different problems. ResNet [15] (Residual Net) was proposed by [PERSON]. Its internal residual block uses skip connections to alleviate the gradient disappearance problem caused by increasing depth in deep neural networks, and it won the ILSVRC champion in image classification and target recognition in 2015. Since then, a variety of computer vision tasks have mainly used ResNet as the backbone network, while increasing the number of feature maps when increasing the dimension [16]. In 2016, the DPRN [17] (Deep Pyramidal Residual Networks) gradually increased the feature map dimension of all units and improved the residual module on the basis of ResNet to build a pyramid-shaped residual network. It has a good application in hyperspectral image classification [18]. The deep network architecture DenseNet [19] proposed by [PERSON] in 2017 proposed a new approach for feature reuse. Each layer in the model gets additional input from all previous layers and passes its own feature map to all subsequent layers to make each layer densely connected to other layers, which not only slows down the phenomenon of vanishing gradients but also reduces the amounts of parameters. [PERSON] proposed SENet [20] (Squeeze and Excitation Network). It is an architectural unit that aims to improve the representational ability of the network by dynamically recalibrating the channel features of the network. SENet performs a squeeze operation on the feature map obtained by convolution to obtain the global feature. Then, it performs an excitation operation on the global feature to learn the relationship between each channel and also obtains the weights of different channels. Finally, the result is multiplied by the original feature map to obtain the final feature.