## 4 Discussion
### Evaluation Metrics
Compared to ResNeXt-101, EfficientNet-B3 slightly underperforms in a few categories, such as \"efflorescence\", but maintains the best overall performance with a Balanced Accuracy of 0.935 and an overall F1-Score of 0.882. The performance differences among the six models can be explained through their architectural depth, parameter count and how these factors interact with the full fine-tuning process. ResNet-18, being the shallowest with around 11 M parameters, offers fast convergence and decent generalization under limited data, but its limited depth may hinder the learning of complex defect features. ResNet-50 and ResNet-101 progressively increase in depth (with \(\sim\)23 M and \(\sim\)45 M parameters, respectively), allowing for more detailed feature extraction but also introducing a higher risk of overfitting. ResNeXt-50 and ResNeXt-101 introduce grouped convolutions (cardinality), which expand feature diversity without significantly increasing the parameters, making them efficient for capturing subtle defect variations. EfficientNet-B3, with compound scaling of the width, depth and resolution, achieves strong performance due to its balanced design, though it is more sensitive to hyperparameter tuning and training dynamics. Under full fine-tuning, where all layers are updated, these structural differences play a more prominent role, as the model's ability to adapt feature hierarchies becomes crucial for multi-label defect recognition. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Class**} & \multicolumn{4}{c}{**ResNeXt-50**} & \multicolumn{4}{c}{**ResNeXt-101**} & \multicolumn{4}{c}{**EfficientNet-B3**} \\ \cline{2-13}  & **Precision** & **Recall** & **F1-Score** & **Enhanced Accuracy** & **Precision** & **Recall** & **F1-Score** & **Enhanced Accuracy** & **Precision** & **Recall** & **F1-Score** & **Enhanced Accuracy** \\ \hline Crack & 0.825 & 0.840 & 0.879 & 0.959 & 0.858 & 0.933 & 0.883 & 0. CNN-based models on the CODEBRIM dataset. Models from previous studies, including MetaQNN [24], VGG16, Inception-V3 and ResNet-50 [30], have reported accuracies ranging from 72.19% to 90.00%. In contrast, the models implemented in this study, ResNet-18, ResNet-50, ResNet-101, ResNeXt-50, ResNeXt-101 and EfficientNet-B3, consistently outperformed earlier methods, achieving accuracies from 90.90% to 93.60%. This performance gain can be attributed not only to the use of deeper and more advanced network architectures but also to the application of full fine-tuning, which allowed the models to better adapt to the dataset-specific features. Moreover, the use of the focal loss function, which emphasizes harder and minority class examples during training, contributed to improved classification performance, especially in cases of class imbalance. Among them all, ResNeXt-50 achieved the highest accuracy (93.60%), followed closely by EfficientNet-B3 and ResNeXt-101 (both at 93.50%). Table 7 shows that modern architectures can significantly outperform traditional models in multi-label bridge defect classification. This highlights the importance of architectural choice and tuning strategies in real-world infrastructure monitoring systems.