## 5 Conclusions  
Periodic visual inspections are currently conducted to maintain the structural integrity of railway bridges. These inspections typically require human inspectors to visually assess damage using specialized equipment, such as aerial ladders. However, this traditional approach poses safety risks and requires substantial time and budget. Therefore, a more efficient and reliable visual inspection method utilizing unmanned aerial vehicles (UAVs) is necessary. This study proposed and evaluated a damage detection model based on the U-Net deep learning framework, using UAV-captured imagery to replace manual inspections. The goal was to identify a method suitable for detecting and classifying multiple types of damage under real-world environmental conditions, even with limited training data. To improve performance, the study compared the proposed U-Net model with IoU Loss to models using Cross-Entropy Loss and Focal Loss, aiming to develop a robust multi-class damage detection approach specifically tailored to the needs of Korea's infrastructure maintenance industry. Five major types of surface damage--cracks, concrete scaling/spalling, water leakage, exposed rebar, and paint peeling--were used to generate ground-truth (GT) training data and evaluate detection performance. A field-applicable damage detection process was developed by implementing three different loss functions in a U-Net architecture to detect and classify multi-class damage on railway bridges. The key conclusions are summarized as follows:  
1. The proposed model successfully detected and classified five key types of damage commonly assessed during visual inspections for railway bridge safety, even under complex and overlapping conditions. 2. The U-Net model trained with Focal Loss outperformed the one using Cross-Entropy Loss, particularly in detecting concrete spalling and water leakage. The Recall values of the Cross-Entropy-based model for cracks, concrete spalling, exposed rebar, leakage, and paint peeling were 0.776, 0.452, 0.851, 0.374, and 0.903, respectively. With Focal Loss, these improved to 0.834, 0.807, 0.760, 0.846, and 0.965, indicating substantial enhancement, especially in underrepresented classes. The F1-Scores were also noticeably improved. 3. The application of Focal Loss helped mitigate issues arising from class imbalances in training data. By adjusting class weights, the model proved suitable for field scenarios where data for certain damage types may be scarce. Despite these improvements, challenges remained in effectively learning underrepresented damage classes. To address this, the IoU Loss function was applied to strengthen weighting on overlapping areas, resulting in further performance gains. The final model, which applied the IoU Loss function to the U-Net architecture, achieved an average Recall of 0.914 and an F1-Score of 0.913, representing a 34.2% improvement in Recall and a 26.0% improvement in the F1-Score compared to the baseline U-Net model. The proposed IoU-Loss-based U-Net model effectively detects and classifies complex, multi-class damage using UAV imagery under real-world environmental conditions, with strong resistance to false positives caused by noise or natural background features. This method provides a more objective and precise alternative to conventional manual inspections. As the railway infrastructure maintenance sector increasingly adopts UAV-based automation for structural inspections, the proposed approach is expected to contribute to improved accuracy, reduced inspection time, and cost savings, ultimately supporting the advancement of the industry. While this study focused on five surface damage types, future work should expand to include efflorescence and corrosion of steel components, which are critical to structural safety. Continued validation and testing will be pursued with the goal of fully replacing manual visual inspections in the field.