# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 5 Conclusions and Future Work  
In this study, various pretrained convolutional neural network (CNN) models were evaluated for the detection of pile defects in low-strain pile integrity test (LSPIT) reflectograms. The results demonstrated that modern architectures such as _ConvNeXtLarge_ and _ResNet152V2_ achieved superior performance, with _ConvNeXtLarge_ achieving the highest accuracy of 0.982 and an AUC of 0.963. These models effectively leveraged transfer learning by utilizing frozen feature extraction layers while incorporating custom classification layers to adapt to the dataset. The lightweight _MobileNetV2_ model also performed competitively, offering an optimal balance between computational efficiency and classification accuracy. In contrast, older architectures such as _VGG19_ and _ResNet50_ struggled to generalize, resulting in lower precision and recall values, highlighting their limitations in handling complex defect patterns. The confusion matrix and ROC analysis further emphasized the reliability of modern CNN models in accurately detecting defective piles while minimizing false positives. The _ConvNeXtLarge_ model achieved perfect precision in identifying intact cases while maintaining a recall of 0.927 for defective samples, making it the most suitable choice for practical deployment scenarios. However, some models, such as _EfficientNet_, failed to generalize to the dataset, consistently predicting all samples as intact, leading to their exclusion from further analysis. Despite the promising results obtained in this study, several areas warrant further investigation. First, fine-tuning selected layers of the pretrained models instead of freezing all layers could potentially enhance feature extraction and improve classification performance. Second, exploring data augmentation techniques tailored to reflectogram images, such as synthetic defect generation or domain-specific transformations, could help improve generalization. Additionally, expanding the dataset with more diverse and representative samples from different pile types and environmental conditions could provide better model robustness. While the proposed method demonstrates high accuracy, its practical deployment must balance considerations of computational resources, processing speed, and interpretability. Additionally, limitations include potential sensitivity issues regarding minor or previously unobserved defect types. The current model demonstrates limitations in reliably detecting very minor defects, such as narrow cracks or subtle discontinuities, indicating a need for more sensitive feature extraction methods and broader defect coverage in future training datasets. Future work should also focus on optimizing inference speed for real-time applications by employing model quantization and pruning techniques to reduce computational overhead without compromising accuracy. Moreover, integrating the trained models into an end-to-end pipeline for automated pile integrity assessments, incorporating data pre-processing, anomaly detection, and report generation, would enhance practical usability. Lastly, investigating hybrid approaches that combine CNN-based feature extraction with classical machine learning techniques, such as ensemble learning or anomaly detection algorithms, could further improve detection reliability. Future research could benefit from publicly available datasets and standardized benchmarks, encouraging reproducibility and fostering broader community validation. In conclusion, the findings of this study demonstrate the effectiveness of transfer learning with modern CNN architectures in pile defect detection and provide a solid foundation for future improvements. The insights gained can aid industry professionals in selecting suitable models for real-world applications while paving the way for further advancements in the field.