# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 4 Results and Discussion
Analysis of the misclassified defective cases revealed subtle defects characterized by minimal signal variations, emphasizing the model's potential limitations in detecting such  
Figure 4: Confusion matrices of all pretrained CNN models. Figure 5: Confusion matrix of the best-performing model (ConvNeXtLarge). nuanced anomalies without additional targeted training. Detailed analysis of misclassified reflectograms revealed that missed defective cases typically exhibited subtle amplitude variations or minimal reflections due to minor structural anomalies. These findings underscore the necessity of expanding the training dataset with diverse subtle defect cases. The Receiver Operating Characteristic (ROC) analysis further highlights the classification capabilities of the models, as shown in Figures 6 and 7. ConvNeXtLarge achieved the highest AUC value of 0.963, reinforcing its superior ability to differentiate between defective and intact piles. ResNet152V2 and MobileNetV2 followed with AUC values of 0.947 and 0.939, respectively, demonstrating their potential for practical applications. On the other hand, VGG19 and ResNet50 had lower AUC values of 0.720 and 0.571, respectively, indicating limited discriminatory power. A comprehensive evaluation of model performance using various metrics, including accuracy, precision, recall, F1 score, and AUC, is provided in Figure 8. ConvNeXtLarge again demonstrated its dominance with an accuracy of 0.982, perfect precision of 1.000, and an F1 score of 0.962. ResNet152V2 and MobileNetV2 followed closely, maintaining high scores across all evaluation metrics. In contrast, older models such as VGG19 and ResNet50 exhibited lower precision and recall, underscoring their limitations in handling complex datasets. In addition to accuracy, we reported precision, recall, and F1 score metrics explicitly to provide a comprehensive performance evaluation, especially critical for minority defect classes. For the ConvNeXtLarge model, precision was 100%, recall was 92.7%, and the F1 score was 96.2%. Misclassification analysis indicated that defective samples missed by the model typically displayed subtle features, challenging the model's detection capabilities under current training limitations. Figure 6: ROC curves of all pretrained CNN models. Overall, the results indicate that models such as ConvNeXtLarge, ResNet152V2, and MobileNetV2 offer an optimal balance of performance and efficiency. These findings emphasize the importance of selecting architectures that align with dataset complexity and computational constraints to achieve reliable pile defect detection. Future work may focus on fine-tuning and ensembling techniques to further enhance model performance. Figure 8: Performance comparison of different CNN models based on various metrics. Figure 7: ROC curve of the best-performing model (ConvNeXtLarge) with an AUC of 0.963. Statistical comparisons using [PERSON]'s test confirmed that the ConvNeXtLarge model's performance improvements over other architectures were statistically significant (\(p<0.05\)). This study distinguishes itself by using real field-acquired data, systematically evaluating the performance of several pretrained CNN architectures, and discussing the models' generalization capabilities in a practical LSPIT context. These aspects contribute to a more realistic and replicable foundation for future research and deployment in real-world integrity testing scenarios. Regarding deployment feasibility, preliminary computational complexity analysis indicated that ConvNeXtLarge requires significant computational resources (approximately 15.4 GFLOPs), potentially limiting real-time embedded implementation. However, lighter architectures like MobileNetV2 (approximately 0.3 GFLOPs) present feasible alternatives. Strategies such as model quantization, pruning, and knowledge distillation could optimize ConvNeXtLarge for practical, on-site embedded deployments, which merits detailed investigation in subsequent research.