# Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
## 4 Conclusions  
In this study, in addition to the existing U-Net deep learning algorithm, we implemented a bridge-specific BIRD U-Net algorithm through epoch setting, Adam function, and bilateral filtering. We also evaluated the performance of BIRD U-Net, U-Net, and ResNet algorithms using a chaotic matrix-based model evaluation method. The results demonstrated that BIRD U-Net has a higher detection recall rate than U-Net and ResNet in all test cases, with an average of 96% for BIRD U-Net, 92% for U-Net, and 71% for ResNet. BIRD U-Net and U-Net exhibited the largest increases in detection recall by damage type, with efflorescence at 7% and corrosion at 5%, whereas cracks, delamination, and rebar exposure averaged a 2% increase. When comparing BIRD U-Net to ResNet, corrosion exhibited the largest increase of 32% and efflorescence of 27%, whereas cracks, delamination, and rebar exposure exhibited an average increase of 21%. This result is attributable to ResNet having a segmentation feature that detects the complete shape of the bridge by judging the shape and color of the bridge, in contrast to the BIRD U-Net algorithm. Maintaining domestic facilities is difficult due to the continuous increase in the number of facilities to be managed and the decrease in the number of personnel in charge. Specifically, most facilities require thorough management due to various natural disasters and aging structures, and it is important to avoid disasters caused by poor management. In addition, bridges, which are vulnerable to access, require systematic management because they are major civil engineering structures for railways. Visual inspection is the primary method for investigating the appearance of structures. However, inspecting the exterior of a structure for damage is expensive and time-consuming due to the operation of separate survey equipment in inaccessible environmental conditions. In this study, various methods of deep learning-based automatic damage analysis techniques were reviewed to automate the damage inspection of 33,924 images taken by a UAV to more efficiently and reliably inspect the exterior of bridges using UAVs. Damage items were defined as cracks, efflorescence/leakage, delamination, corrosion, and rebar exposure, based on the images acquired by operating a commercial UAV equipped with a high-resolution camera, and deep learning analysis models were created by extracting damage as training data. In this study, damage images directly acquired using commercial UAV and other image data on damage occurring in various concrete structures were obtained and applied to develop deep learning models. The results revealed that BIRD U-Net has a higher detection recall rate than U-Net and ResNet in all test parts, with an average of 96%, with U-Net and ResNet having an average of 92% and 71%. By applying automatic damage analysis technology, as reviewed above, more objective and accurate damage detection can be achieved compared to previous visual inspection. Moreover, an efficient maintenance system can be established. In addition to bridges, the BIRD U-Net algorithm can be used to perform image analysis on other concrete structures. Therefore, the automatic damage analysis technique for bridges developed through this research can provide objective damage inspection results for regular inspections using unmanned mobile vehicles in the field of concrete structure maintenance while reducing the time and cost compared to utilizing the existing system. Various methods other than the U-Net-based algorithm proposed in this study are being explored for damage inspection using UAVs. Additional research is needed to develop more effective algorithms. In order to create and apply an algorithm for selecting an optimal maintenance method, it is necessary to enhance the accuracy of influencing factors beyond their current levels. These influencing factors may encompass the collection of a sufficient amount of damaged data.