# Landslide Recognition Based on DeepLabv3+ Framework Fusing ResNet101 and ECA Attention Mechanism
## 4 Experiments and Results
### Backbone Network Selection
#### 4.2.2 Statistical Significance Analysis  
\(p\)-values are a commonly used method in statistical hypothesis testing for assessing the compatibility of data with hypotheses, and they can be used in deep learning to assess the statistical significance of a model or to perform hypothesis testing. During the run, we performed 10 statistical validation experiments, and each time, the average accuracy was preserved by 4 bits. Table 4 records the average accuracy over 10 experiments. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Backbone** & **Average Training Time (s)** & **Max GPU Memory Usage (MB)** & **Average Inference Time per Image (s)** & **FLOPs (G)** & **Total Number of Parameters** \\ \hline DeepLabv3+-MobileNet & 5.79 & 599.52 & 0.0127 & 2.76 & 10,507,730 \\ DeepLabv3+-VGG16 & 38.52 & 2979.57 & 0.0040 & 5.52 & 19,156,706 \\ DeepLabv3+-Xception & 10.2 & 1620.31 & 0.0117 & 8.28 & 35,864,490 \\ DeepLabv3+–ResNet18 & 5.46 & 641.30 & 0.0073 & 3.5 & 16,606,434 \\ DeepLabv3+–ResNet50 & 23.59 & 2426.75 & 0.0140 & 11.04 & 42,002,327 \\ DeepLabv3+-ResNet101 & 33.91 & 3894.33 & 0.0190 & 16.008 & 60,994,455 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Computational cost when training different backbones. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Backbone** & **Precision** & **Recall** & **F1 Score** & **MIoU** \\ \hline DeepLabv3+ & 0.9234 & 0.9059 & 0.9144 & 0.7367 \\ DeepLabv3+-MobileNet & 0.8804 & 0.8757 & 0.8780 & 0.6357 \\ DeepLabv3+-VGG16 & 0.9201 & 0.8906 & 0.9046 & 0.7004 \\ DeepLabv3+-Xception & 0.9063 & 0.8991 & 0.9026 & 0.7029 \\ DeepLabv3+-ResNet18 & 0.9293 & 0.8990 & 0.9134 & 0.7347 \\ DeepLabv3+-ResNet50 & 0.9241 & 0.9060 & 0.9147 & 0.7338 \\ DeepLabv3+-ResNet101 & 0.9283 & 0.9160 & 0.9220 & 0.7552 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Evaluation metrics for different backbone networks. It can be seen from the figure that DeepLabv3+-ResNet101-ECA has lower loss and higher accuracy on the validation set, and the evaluation index values of the above models are shown in Table 6. It can be determined from the results in the table that the DeepLabv3+-ResNet101-ECA model has the highest precision, recall, F1 score and MIoU, which are 0.9351, 0.9259, 0.9240 and 0.7603, respectively. And the lowest value in the table increased by 1.09%, 1.64%, 0.68%, and 1.51%, respectively. In the experiment, six landslide samples in the test set were selected and labeled as landslide I, II, III, IV, V and VI, respectively, as shown in Figure 11. For landslide I, the predicted mask edge blur of DeepLabv3+-Reset101-BAM and DeepLabv3+-Reset101-SE has a large difference. The prediction mask of DeepLabv3+-Reset101-CBAM is smaller than the true mask. For landslide II, III and VI, all four models show certain recognition performance. For landslide IV and V, the DeepLabv3+-Reset101-ECA model is more consistent with the real mask than the other models. In general, the DeepLabv3+-Reset101-ECA model has the best performance and the predicted mask is more consistent with the true mask, with high accuracy. As shown in Figure 11, the area selected by the blue box is the false positive area, that is, the area that incorrectly identifies the non-landslide area as a landslide. The DeepLabv3+-ResNet101-ECA model significantly reduces this type of error, which is very important for optimizing resource utilization and improving the credibility of the warning system. The area selected by the green box is the area with clearer detail capture, which reflects the improvement of the proposed model in capturing the subtle features of landslides, which improves the overall recognition accuracy and helps to comprehensively understand the landslide event. The region selected by the red box is the boundary-preserving region. The DeepLabv3+-ResNet101-eca model can delineate the landslide boundary more accurately in this area, which indicates that the new model has higher resolution and accuracy when dealing with complex terrain features.