# Research on Intelligent Recognition Method of Ground Penetrating Radar Images Based on SAHI
## 3 Results
### Experimental Results of the Improved YOLOv5 Model
#### 3.2.3 Test Results  
After training based on the four types of datasets, the model has good detection performance and the detection results obtained by applying them to the test set are shown in Table 4. It can be seen from Table 4 that the mAP50 of dataset I, which contains only the original image, is only 63.3%, while the mAP50 of dataset II, which adds the image generated by using DCGAN based on dataset I, is 68.6%, an increase of 5.3%, indicating that the use of DCGAN can effectively expand the dataset to improve the detection accuracy. Dataset III is sliced by using SAHI to slice the training set and validation set in the training stage, and the final mAP50 is 66.3%, which is 3% higher compared to dataset I, indicating that slicing the images is conducive to the training of the model and thus improves the detection accuracy. Dataset IV, which includes both the dataset-sliced slices and the images generated using DCGAN, has the highest final mAP50 of 70.9%, an improvement of 7.6%. Based on precision (P) and recall (R), the PR curve can be plotted. The comparison of the PR curves is plotted in Figure 9, where both datasets II and III show a small increase in recall compared to dataset I, while dataset IV shows a much higher recall and precision. Table 5 shows the experimental results after using SAHI in the inference stage. From the table, it can be seen that after using SAHI in the inference stage, the mAP50 of dataset I reaches 66.9%, which is an increase of 3.6%, indicating that the use of SAHI in the inference stage is very effective, and that slicing the image increases the relative scale size of the target, enhancing the feature expression of the target and leading to an increase in the detection accuracy. The accuracy of dataset IV after using SAHI in the inference stage, compared with dataset I without SAHI, displayed a great improvement as the mAP50 increased by 10.8%, to 74.1%. \begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **mAP50/\%** \\ \hline I & 0.633 \\ II & 0.686 \\ III & 0.663 \\ IV & 0.709 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results. \begin{table}
\begin{tabular}{c c} \hline \hline
**Dataset** & **mAP50/\%** \\ \hline I & 0.669 \\ IV & 0.741 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Experimental results after using SAHI in the inference stage. Figure 10 shows the PR curves of the experiment after using SAHI in the inference stage, and it can be seen that the recall rate increases substantially after using SAHI in the inference stage. To objectively evaluate the performance of the model in this paper and to further illustrate the superiority of using DCGAN-generated images to expand the dataset and using SAHI in the training and inference stages, the model is evaluated using the ROC and AUC metrics. The ROC curve is an important metric for measuring the performance of an algorithm. The area under the curve (AUC) can be used as a quantitative evaluation metric for the ROC curve. In general, the larger the AUC, the better the detection of the ROC curve. Figure 11 shows the ROC curves for dataset I and dataset IV after using SAHI in the inference stage, respectively. From the ROC curves, it can be seen that the AUC of dataset IV using SAHI in the inference stage is 0.86, while the AUC of dataset I is 0.77, thus verifying the superiority of the improved model. Figure 12 shows the visualization of the results after the detection of the test set. In terms of detection accuracy, it can be seen from the comparative effects in Figure 12a-e that the detection accuracy of the model trained on the dataset after data augmentation is improved, and then additionally, the detection accuracy is improved even more significantly after combining SAHI in the training and inference stages. In terms of the leakage situation, it can be seen from Figure 12b,d,e that the model trained on dataset I fails to recognize the  
Figure 10: PR curve after using SAHI in the inference stage. Figure 9: PR curve diagram.