# Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
###### Abstract  
Bridge inspection methods using unmanned vehicles have been attracting attention. In this study, we devised an efficient and reliable method for visually inspecting bridges using unmanned vehicles. For this purpose, we developed the BIRD U-Net algorithm, which is an evolution of the U-Net algorithm that utilizes images taken by unmanned vehicles. Unlike the U-Net algorithm, however, this algorithm identifies the optimal function by setting the epoch to 120 and uses the Adam optimization algorithm. In addition, a bilateral filter was applied to highlight the damaged areas of the bridge, and a different color was used for each of the five types of abnormalities detected, such as cracks. Next, we trained and tested 135,696 images of exterior bridge damage, including concrete delamination, water leakage, and exposed rebar. Through the analysis, we confirmed an analysis method that yields an average inspection reproduction rate of more than 95%. In addition, we compared and analyzed the inspection reproduction rate of the method with that of BIRD U-Net after using the same method and images for training as the existing U-Net and ResNet algorithms for validation. In addition, the algorithm developed in this study is expected to yield objective results through automatic damage analysis. It can be applied to regular inspections that involve unmanned mobile vehicles in the field of bridge maintenance, thereby reducing the associated time and cost.  
unmanned aerial vehicle; automatic damage analysis; bridge; deep learning; maintenance 2020 IEEE/ACM Transactions onof separate inspection equipment, such as ladder trucks and refractive skies, in environments with weak access. Various studies to develop methods for efficiently inspecting and maintaining structures are underway. While AI crack detection can be a powerful and efficient way to monitor the structural health of bridges, there are some cons and difficulties associated with approach. Image quality can vary due to factors like weather conditions, lighting, and camera specifications. Poor-quality images may affect the accuracy of crack detection algorithms [3]. UAVs can capture a large volume of data, which may require significant storage and processing capabilities. Handling and analyzing these data can be challenging, especially for large bridge networks. In addition, annotating UAV images for crack detection can be a time-consuming and costly process, which may require manual marking of cracks in images to create a labeled dataset for training AI models. In particular, research on defect inspection in bridges and concrete structures using unmanned mobile vehicles is being actively conducted at home and abroad. In this paper, we aimed to solve existing problems in the industry, as well as those identified in a theoretical review of facility safety diagnosis and maintenance, and to identify the current status of bridges. Furthermore, we propose an objective damage-type classification method utilizing artificial intelligence algorithms based on bridge image data acquired using drones and BIRD U-Net, which complements the U-Net algorithm.