# Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements
## 3 Film Poster Layout Segmentation Method Based on Improved DeepLabv3+
### Improved DeepLabv3+ Network Models  
In order to effectively extract the semantic information of movie posters at different scales, the DeepLabv3+ structure is used in this paper. The algorithmic model proposed in this paper is obtained by improving the DeepLabv3+ algorithmic model, which contains encoder and decoder modules. The encoder module contains the main body of the backbone network (DCNN) with null convolution as well as the ASPP (Null Spatial Convolutional Pooling Pyramid, Atrous Spatial Pyramid Pooling) module, in which the main body of DCNN adopts the improved Xception network with cavity convolution, which mainly adopts deep separable convolution, making its computational volume smaller. On this basis, the shallow features generated by the DCNN are utilized and fed into the decoder-encoder module, and the high semantic high-level features are fed into the decoder for up-sampling, followed by the fusion of the features using the results obtained by \(1\times 1\) convolution, the extraction of the features using \(3\times 3\) convolution, and the bilinear interpolation of the image to obtain segmentation predictions consistent with the image size.  
Due to the more complex structure of the backbone network Xception in the original DeepLabv3+ model, this is more difficult for extracting smaller categories and textures  
Figure 3: Mobilenetv3 network structure diagram (adapted from [17]).  
in complex layout images, and when the network is extracting details and texture information, problems such as blurring and confusion may occur, resulting in the inability to continuously extract a particular category. In addition, the network has more parameters, is more computationally intensive, and takes longer to reason. In this paper, the lightweight Mobilenetv3-small network structure will be used as the feature extraction network, which has a reduced number of parameters relative to the Xception network, as well as a reduced number of computations and time required, thus increasing the speed of training and learning.  
The combination of null rates for the ASPP module null convolution in the original DeepLabv3+ is 6, 12, and 18, and as the backbone network carries out feature extraction, the resolution of the feature maps decreases gradually, and the combination of 6, 12, and 18 cannot extract the features of the multi-resolution feature maps more efficiently because it does not set a smaller null rate, resulting in a lack of the ability to segment the small targets. In order to effectively extract the semantic information of complex layout images at different scales, the DeepLabv3+ structure is improved in this paper. In the improved DeepLabv3+ model, the original backbone network Xception is replaced by Mobilenetv3-small network, and at the same time two null convolution layers are added in the ASPP module. The null convolution null rate in the module is changed from a combination of 6, 12, and 18 to a combination of 2, 4, 8, 12, and 16, in order to be able to enhance the ability of the network model for the segmentation of different size categories in complex layout images. It is processed by two modules, the backbone network and ASPP, and transmitted to the decoder module, whose main structure remains unchanged. After the complex layout image processed by decoder module will output the predicted layout element segmentation, then the prediction result will be passed to the GoogLeNet classification model to classify the layout, and finally obtain the result prediction of the layout mode of the movie poster. The structure of the improved DeepLabv3+ model is shown in Figure 4.