# Transfer Learning-Based Detection of Pile Defects in Low-Strain Pile Integrity Testing
## 4 Results and Discussion  
All experiments were conducted on a high-performance workstation equipped with an Intel i9-11900 CPU, an NVIDIA Quadro RTX 4000 GPU, and 128 GB of RAM, running Pop!_OS, a Linux-based operating system optimized for performance. The system's high computational capacity facilitated efficient training and evaluation, leveraging GPU acceleration via TensorFlow and Keras for deep learning computations. Data preprocessing and analysis were carried out using Python 3.10 libraries such as NumPy 2.1 and Pandas 2.2. During the initial experimentation phase, the EfficientNet models were considered; however, they were excluded from further analysis due to their poor performance in the pile defect detection task. These models consistently predicted all samples as intact, indicating an inability to generalize to the characteristics of reflectogram images. Despite their success  
Figure 2: Proposed model architecture: Feature extractor with frozen layers and custom classification head. in other vision applications, EfficientNet models were unsuitable for this specific dataset. Models exhibiting poor convergence or substantial underfitting (due to excessive freezing of layers or limited model complexity) were excluded from further detailed analysis after preliminary evaluations. The accuracy comparison of various pretrained CNN models is illustrated in Figure 3 (parameter counts explicitly provided: ConvNeXtLarge: 197M, ResNet152V2: 60M, MobileNetV2: 3.4M). ConvNeXtLarge achieved the highest accuracy of 0.982, showcasing its superior feature extraction capabilities enabled by modern architectural enhancements such as depthwise convolutions and improved normalization. The high accuracy achieved by ConvNeXtLarge can be attributed to its efficient handling of spatial hierarchies and detailed feature extraction capabilities. Despite the inherent noise and heterogeneity present in real-world reflectograms, ConvNeXtLarge effectively captured discriminative features, demonstrating robustness suitable for practical field conditions. ResNet152V2 and MobileNetV2 followed closely with accuracies of 0.970, demonstrating a good balance between performance and efficiency. Conversely, older architectures such as VGG19 and ResNet50 performed poorly, with accuracies of 0.750 and 0.759, respectively, highlighting their limitations in handling complex defect detection tasks. MobileNetV2, despite its lower complexity compared to ResNet152V2, performed similarly due to its optimized architecture for mobile and resource-constrained scenarios, effectively capturing sufficient discriminative features without excessive parameters. This suggests that pile defect features may not require extremely deep or complex architectures, thus highlighting practical trade-offs between model complexity and performance. Lightweight models such as MobileNetV2 exhibited competitive accuracy while being computationally efficient, making them suitable for deployment in resource-constrained environments. In contrast, deeper architectures like ResNet152V2 benefited from residual connections that enhanced feature learning. The DenseNet models provided stable yet moderate performance, while NASNet models, despite leveraging neural architecture search, were outperformed by more efficient alternatives. Figure 4 presents confusion matrices for all models, while Figure 5 focuses on the best-performing ConvNeXtLarge model. The ConvNeXtLarge model demonstrated outstanding classification performance, correctly identifying all intact samples and misclassifying only 6 defective cases, resulting in a recall of 0.927 for the positive class. In contrast, models such as VGG19 exhibited higher false positive rates, misclassifying 54 intact samples as defective,  
Figure 3: Accuracy comparison of different pretrained CNN models. leading to a decline in precision. Deep ResNet variants achieved improved precision and recall compared to their shallower counterparts.