# Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
## 2 Previous Research
### Reviewing Prior Research
However, because most of the research in civil engineering aims to identify accident hazards, perform real-time situation analysis, and locate defects based on data acquired from CCTV or fixed sensors, the emphasis is on research and development by utilizing various types of data in the field of civil engineering. [PERSON] et al. (2021) [15] proposed a multi-damage-type detection system for bridge structures using a CNN- and pixel-based watershed region segmentation algorithm considering the problems in objectivity and efficiency arising from trained inspectors making visual judgments in the field during structure safety inspection under current methods. [PERSON] et al. (2021) [16] proposed a deep learning-based image preprocessing and bridge damage object automation method for automating bridge damage inspection, considering that bridge surface defects are manually inspected and intuitive judgment is the evaluation factor. [PERSON] (2023) [17] endeavored to amalgamate visual inspections with automated defect recognition for evaluating the condition of heritage bridges, particularly reinforced concrete (RC) structures. The research compiled images of common defects and applied convolutional neural networks (CNNs) for defect identification. Notably, the study prioritized rendering the outcomes comprehensible and applicable by employing class activation maps (CAMs) and explainable artificial intelligence (XAI) techniques. It also assessed the efficacy of different CNN models and introduced innovative metrics for reliability. These findings hold practical significance for road management entities and public organizations involved in gauging the health of road networks. Recently, different organizations have been promoting the use of unmanned mobile vehicles for efficient management in maintaining these concrete facilities through pilot operations and applicability reviews to acquire exterior images of bridge facilities. Accordingly, the inspection time for the structure exterior has been shortened compared to when performing a manual inspection. However, damage inspection during a safety review is again performed by a human. Therefore, inspections using unmanned mobile vehicles are not limited to simply photographing the exterior, but can be extended to detecting damage from the acquired images. Furthermore, they can overcome the problems of manpower requirements and limited access, which limit the safety inspection of the exterior of the structure under current methods. As a key technology, this method can be a step in developing an effective maintenance system by reducing the high manpower, time, and cost requirements of processing the acquired exterior images. To inspect bridge facilities using unmanned vehicles, an unmanned vehicle control system and operation automation system that is suitable for the railway operation environment is required, and the necessary technologies for practical and efficient inspection must be utilized, such as those required to address inaccuracies in shadowed areas when relying on GPS for existing unmanned vehicles and automation of damage detection and analysis from acquired images  
In this study, the damage items to be learned and analyzed through deep learning-based image analysis technology were first defined from the exterior damage of bridges, which represent one of the vulnerable access facilities. Figure 1 demonstrates the damage items that can be collected using unmanned mobile vehicles. The images in Figure 1 were collected by UAVs, and they were carefully chosen to highlight clear differences in each type of damage. Various types of damage can occur in bridges, such as surface cracks, concrete scaling/spalling, reinforcement exposure, or leaks. Image classes were chosen for each type to train the AI model to recognize specific characteristics of cracks, with images having been captured from different angles, distances, and perspectives. Designating image classes based on these perspectives can enhance the model's ability to detect cracks in diverse situations.