## 4 Analysis and Discussion
### Grad-CAM Visualization  
The Grad-CAM technique provides a deeper understanding of the model's prediction process. By visualizing Grad-CAM heat maps for each layer of the model, it is possible to understand what has been learned and the differences at each stage of the model's feature extraction. This paper selects the last convolutional layer with high feature representation for Grad-CAM heatmap visualization. Unlike other evaluation metrics, the Grad-CAM technique does not directly reveal the performance of a model, but it can reveal the differences in attention of each model, which can be used to guide model optimization.  
By observing the Grad-CAM heat maps of all the models presented in Figure 9, it can be found that the attention of different CNN architectures to the same sample has some similarities, but there are also obvious differences in certain architectures. Specifically, the network models with deeper layers, represented by ResNet and DenseNet, have stronger expressive ability and sensory wildness due to the introduction of residual connectivity, dense connectivity, and other techniques, which pay more attention to the global information of the distressed image during the learning process. As a result, it shows extremely similar attention and more attention areas in the Grad-CAM heat map. In contrast, network models with shallower layers, such as AlexNet and VGG, tend to focus more on the detailed local features of the image. As a result, fewer regions of attention are shown in the Grad-CAM heatmaps, and there is some variation in model attention. Similarly, EfficientNetB2 and MobileNetV3 have deeper network structures and more complex connections compared to SqueezeNet1_1, so pay more attention to the global information of the distress image, again showing more regions of attention. In general, deeper models tend to display more regions of interest on the Grad-CAM heat map, extract high-level semantic features, and achieve better performance. However, Table 5 shows that some shallower network models, such as AlexNet, VGG13, and SqueezeNet1_1, outperform deeper models in the evaluation results. Possible reasons for the observed results include the relatively small size of the GPR B-scan distress image dataset used in this paper, which may lead to overfitting on deeper models. Additionally, some of the shallower networks may focus not only on local features of the image but also on global features. Therefore, although the shallow network models show fewer regions of interest in the Grad-CAM heat map, their overall performance may be relatively better.  
Figure 10 shows some typical and representative misclassified samples. Taking sample-13 as an example, the models that are correctly classified with high confidence are MobileNetV3_16 (89.01%) and SqueezeNet1\(1\)64 (88.98%), while the models that are incorrectly classified with high confidence for the incorrect category are ResNet18_32 (97.84%) and ResNet18_64 (84.35%). The correctly categorized models directed their attention toward the key characteristics of interlayer loosening, whereas the misclassified models focused on features resembling those of interlayer water seepage. This shows that the probable cause of the models' misclassification is the bias of the models' attention.