# Image-Level Anti-Personnel Landmine Detection Using Deep Learning in Long-Wave Infrared Images
## 4 Experimental Results
### Results with Duplicated Train Set  
Using the prediction results on the test set, the confusion matrices for the five foundation models and the binary CNN-based classifier are generated and presented in Figure 7. Since we perform 5-fold cross-validation, as described in Section 2, the sum of the elements in each confusion matrix equals the total number of images in the dataset. The precision, recall, and F1-score for the positive class are calculated and summarized in Table 3. The first  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Augmentation Method** & **Parameters** \\ \hline RandomHorizontalFlip & \(p\) = 0.5 \\ \hline RandomVerticalFlip & \(p\) = 0.5 \\ \hline RandomResizedCrop & size = (h,w),scale = (0.9, 1.0) \\ \hline RandomRotation & degrees = 45 \\ \hline ColorJitter & brightness = 0.1, contrast = 0.1 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Employed data augmentation methods in the Pytroch framework to augment the training images. Height and width of input images are represented by h and w, respectively.  
Figure 6: The learning rate and loss variations in the training process.  
notable observation from the experimental results is that all five vision foundation models, when combined with ABMIL, achieve consistently high precision, recall, and F1-score values. The lowest precision and recall observed are 0.961 and 0.964, respectively, produced by the binary CNN-based classifier. Another important finding is that ConvNeXt, when combined with ABMIL, correctly classifies all positive images (i.e., recall rate is equal to 1) in the dataset. In the context of landmine detection applications, achieving a high recall rate is particularly crucial, as failing to detect positive samples could lead to serious safety risks.  
We use Grad-CAM [52] to visualize the regions of input images that most influence the classification decisions of the trained models. Although visualization methods tailored specifically for ViT exist [53], we apply Grad-CAM across all model architectures to enable direct and consistent comparisons. Figure 8 presents Grad-CAM heatmaps for the five vision foundation models. From top to bottom, the size of the AP landmine in the test images progressively decreases. The heatmaps produced by ResNet closely align with the actual locations of the AP landmines. In contrast, the heatmaps for the other models generally appear unrelated to the target objects. To quantitatively assess the extent to which models rely on spurious features, we evaluate their performance on 591 synthetic landmine-free images, with the results summarized in Table 4. Surprisingly, all models--even ResNet, which visually appears to attend to the foreground--produce a substantial number of false positives. This suggests a strong reliance on spurious features in their decision-making processes.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **BinaryCNN** & **ResNet** & **ConvNeXt** & **ViT** & **OpenCLIP** & **InfMAE** \\ \hline Precision & 0.957 \(\pm\) 0.004 & 0.989 \(\pm\) 0.003 & **0.991 \(\pm\) 0.002** & 0.985 \(\pm\) 0.002 & 0.981 \(\pm\) 0.004 & 0.987 \(\pm\) 0.004 \\ \hline Recall & 0.967 \(\pm\) 0.007 & 0.993 \(\pm\) 0.002 & **1.000 \(\pm\) 0.000** & 0.998 \(\pm\) 0.002 & 0.996 \(\pm\) 0.006 & 0.997 \(\pm\) 0.003 \\ \hline F1-score & 0.962 \(\pm\) 0.003 & 0.991 \(\pm\) 0.001 & **0.995 \(\pm\) 0.001** & 0.991 \(\pm\) 0.001 & 0.989 \(\pm\) 0.002 & 0.992 \(\pm\) 0.003 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Precision, recall, and F1-score of positive class (images with AP landmine) when models are trained with the duplicated train set. The best values are highlighted in bold.  
Figure 7: Confusion matrices when models are trained with the duplicated train set.