## 1 Introduction  
The number of railway bridges is steadily increasing to support the expansion of transportation infrastructure. Consequently, the demand for maintenance and inspection of both newly constructed and existing railway bridges is also on the rise. [PERSON] [1] projected that bridge maintenance will evolve into a large-scale industry as the number of bridges increases over time. Cracking is a commonly observed issue in concrete bridge structures, and if left unmanaged, it may result in significant economic losses and safety hazards. As railway bridges are critical infrastructure elements where safety is paramount, they require systematic and periodic inspection. However, there has been a continuous decline in the available workforce for maintenance operations. Traditionally, inspections are performed manually through visual assessments. Visual inspection remains the most widely used method for monitoring the condition of large structures such as bridges. [PERSON] [2] emphasized the importance of systematic and periodic visual inspections for monitoring the health of aging bridge infrastructure and evaluated various deep learning techniques aimed at automating this process. However, the growing number of structures requiring inspection each year makes efficient management increasingly difficult. Manual inspections often require specialized equipment such as aerial ladders, which increases the risk of safety incidents and demands significant time and financial resources. Various alternative approaches are being explored to address these limitations, and the application of unmanned aerial vehicles (UAVs) for inspecting bridges and concrete structures has gained substantial attention. [PERSON] [3] proposed a UAV-based structural health monitoring (SHM) system that utilizes mounted or embedded sensors to collect data and capture images from multiple angles, enabling early and accurate damage diagnosis. [PERSON] [4] introduced a systematic workflow that integrates UAV flight with Building Information Modeling (BIM) to facilitate autonomous inspection in real environments. [PERSON] [5] highlighted the potential of UAVs as efficient inspection tools, demonstrating their capability to reduce the time and cost of bridge inspections while applying deep learning techniques for damage evaluation. [PERSON] [6] suggested using UAV-mounted sensors for measuring bridge displacements, and [PERSON] [7] advocated for UAV-based inspection combined with digital assessment methods to provide reliable and cost-effective data for the maintenance of industrial buildings. UAVs are particularly useful for inspecting hard-to-reach areas where close-proximity human access is difficult or unsafe. Several studies have explored UAV-based inspection methods for bridges and concrete structures. [PERSON] [8] addressed the limitations of manual inspection by proposing a deep-learning-based model, BDODC-F, for detecting bridge damage. [PERSON] [9] introduced a \"double detection + single segmentation\" approach combining YOLOv5(x) and U-Net for detecting and segmenting cracks in real bridge environments. These methods aim to enhance the objectivity and efficiency of damage inspections that are traditionally reliant on human judgment. High-resolution imagery captured by UAVs enables detailed visual analysis of bridge damage. [PERSON] [10] proposed the Residual Linear Attention U-Net (RLAU-Net) to improve crack detection performance. [PERSON] [11] introduced a trained graph neural network (GNN) framework capable of simulating crack propagation, adhesion, and corresponding stress distributions without additional post-processing. [PERSON] [12] pointed out the difficulty in simultaneously achieving high precision and high efficiency in bridge crack detection and proposed a new deep-learning-based method tailored for urban bridges. [PERSON] [13] evaluated the performance of four pre-trained networks, AlexNet, VGG16, VGG19, and ResNet-50, for concrete crack classification using deep learning.