# Deep Learning-Based Layout Analysis Method for Complex Layout Image Elements
## 5 Experimental Results and Analysis
### Segmentation Comparison Experiment Analysis
#### 5.2.2 Comparative Experimental Analysis of Different Models  
In order to further validate the effectiveness and advantages of the proposed model in semantic segmentation tasks, we selected four widely used models, namely, FCN [20], PSPNet [21], DeepLabv3 [15], and the original DeepLabv3+, for comparative testing. All \begin{table}
\begin{tabular}{c c c} \hline \hline
**Backbone** & **MIoU/\%** & **Time/h** \\ \hline Xception & 71.09 & 25.10 \\ ResNet101 & 72.89 & 26.20 \\ Mobilenetv2 & 70.05 & 16.20 \\ Mobilenetv3-small & **75.60** & **15.09** \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of model results using different backbone feature extraction networks. Boldbest performance. Figure 7: Loss function variation curves of the training set and validator on three different models. (**a**) The improved DeepLabv3+ model in this paper; (**b**) the original DeepLabv3+ model; (**c**) the PSPNet+ResNet50 model. models were trained on the same poster dataset constructed in this study, under identical parameter settings to ensure fairness. The comparative results are presented in Table 3.  
These baseline models represent three distinct architectural philosophies: FCN as an early encoder-decoder framework, PSPNet focusing on global-context modeling through pyramid pooling, and the DeepLab series employing Atrous Spatial Pyramid Pooling (ASPP) combined with a dedicated decoder for multi-scale refinement. Other segmentation frameworks such as UNet [22], BiSeNet [23], and SegFormer [24] were not included in our experiments for specific reasons. UNet, though effective in biomedical imaging, tends to underperform on text-rich natural scenes without extensive enhancements. BiSeNet prioritizes real-time inference speed at the expense of segmentation accuracy, which is suboptimal for our boundary-sensitive poster segmentation task. SegFormer and similar transformer-based models generally require large-scale datasets and considerable computational resources to achieve stable convergence, making them less feasible under our experimental setup and computational constraints. Therefore, our selection of baseline models aimed to strike a balance between representativeness, architectural diversity, and practical feasibility in the context of complex layout image segmentation. From the results in Table 3, it can be seen that the improved model in this study reduces the training time by 45 min compared to the FCN model, and the segmentation accuracy is improved by 2.6%. Compared to the PSPNet model, the improved model in this paper improves 2.0% in MIoU, and the training time is reduced by 78 min; compared to the DeepLabv3 model, the segmentation accuracy of this paper's model has a small effect of improvement. Compared with the DeepLabv3 model, the model in this paper has a small effect of improving the segmentation accuracy, which is only 0.8%, but the training time is saved by 165 min, which is a larger time reduction in comparison; compared with the original DeepLabv3+ model, the model in this paper has a large improvement in the segmentation accuracy, which is 6.1% compared to the improvement, and the training time is also relatively reduced by 52 min. In summary, the performance of the improved model in this study on the self-built dataset is the most outstanding compared to the classical model. As can be seen from Figure 8, although the DeepLabv3 model has a higher MIoU performance compared with other classical models during training, the generalization ability of its model is not high as can be seen from the test result graphs. The FCN model is not fine enough for some detail segmentation, and the learning effect of the layout style model with fewer data samples is not as good as that of the improved model in this study. Compared with PSPNet and the basic DeepLabv3+ model, the difference in segmentation accuracy between the improved models in this paper is not large, and from the visualization results, the segmentation fineness of the improved models in this study is better than that of PSPNet and DeepLabv3+.