## 2 Background  
### Existing Work on Bridge Damage  
A previous study [8] used a web scraping technique to collect various concrete images (damaged or intact concrete images) for training an artificial neural network (ANN), and thus constructed a training image dataset. AlexNet--a DL-based image classifier--was utilized as the initial model to develop an ANN that enabled the automatic detection of spalling in an image using data learned by applying the concept of transfer learning. This model detected concrete scaling/spalling.  
In a study on the automatic damage analysis of railway bridges based on a UAV and a DL model [1], damage types such as cracks, scaling, spalling, water leakage, and reinforced concrete (RC) exposure were detected using a deep-learning model. For training image data, approximately 26,476 images of a level C bridge structure or lower with several defects were collected using a camera. As conventional image-processing techniques, such as pattern recognition and edge detection, erroneously detected defects due to contamination of the damaged surfaces, the study applied a DL-based model. Tools such as sharpening, blurring, and hue/saturation were used to perform image preprocessing, and DL algorithms such as Light Head-RCNN, FPN, PSPNet, and U-Net were used for the investigation. Out of approximately 26,476 images of bridge damage, 80% were randomly selected for model training, and the remaining 20%, 5295 images, were used for testing. When U-Net was used, the detection recall was as follows: cracks 96%, concrete scaling/spalling 98%, rebar exposure 98%, and water leakage 92%. In the study, similar types of damage were grouped into a single type, and one model was trained with multiple types of damage.  
Another study that used DL to evaluate the condition of a bridge [2] highlighted its limitations in identifying multiple types of bridge damages, such as cracks, spalling, and corrosion. To overcome this, methods of training a model for a specific type of damage and applying techniques such as clustering or building a large amount of training data, including a wide range of damage types for classification, were considered as solutions. The study employed a data augmentation technique that increased the volume of training data by applying transformation methods such as image segmentation and rotation, along with transfer learning, to address the problem. As for the DL models used, Mask R-CNN and U-Net were compared in terms of model performance. For training image data, up to 1024 \(\times\) 1024-pixel images were used, and the sliding window method was used to address the problems of damage exposure and quantification. Through experiments, Mask R-CNN was determined suitable for damage detection, as the input size was 1000 \(\times\) 800 pixels, which is relatively large. The model also allowed transfer learning with a limited volume of training data. A final total of 5140 images were used for training in the experiments, and a precision of 95.2% and recall of 93.8% were obtained. The study presented a solution to the limitations in classifying multiple types of damage by increasing the volume of training data, which exhibited robust performance. In contrast, our approach presents a method of model combination after classifying into each type of bridge member. Becauseour approach creates a detection model optimized for each bridge member, high accuracy of detection can be expected.