# Analytical Method for Bridge Damage Using Deep Learning-Based Image Analysis Technology
## 2 Previous Research  
### Reviewing Prior Research  
Prior research on bridge safety inspection using drones has been conducted. [PERSON] et al. (2015) [4] used an unmanned aerial vehicle (UAV) to visually inspect bridges and acquired images of bridge defects using infrared and conventional cameras. [PERSON] et al. (2014) [5] proposed an automated bridge inspection approach that utilizes wavelet-based image features in conjunction with support vector machines to identify cracks in bridges and inspect bridge conditions. [PERSON] et al. (2012) [6] evaluated the use of drone technology as a safety inspection tool and proposed autonomous navigation, voice interaction, a high-resolution camera, and a collaborative user interface environment for unmanned vehicles as a tool for safety checks by managers at construction sites. [PERSON] et al. (2017) [7] reviewed the applicability of unmanned aerial systems (UASs) for safety inspections and developed a set of procedures and guidelines for collecting, processing, and analyzing safety requirements using UAS visual information for safety management. [PERSON] et al. (2016) [8] proposed a smart maintenance plan utilizing advanced convergence technologies, such as drones, IoT, and big data, considering that aging infrastructure threatens safety and increases maintenance demand. [PERSON] et al. (2017) [9] found that an overhaul of the Saemangeum sea wall, a large-scale facility, took two to three months for a conventional visual inspection but one or two days for a drone inspection. [PERSON] et al. (2021) [10] performed 3D mapping using drones to estimate the amount of construction work required because existing facility management methods are risky and require a large amount of time, manpower, and equipment. Their method achieved an accuracy rate of 90.5% compared to the pre-planned amount of construction work. [PERSON] et al. (2023) [11] introduced an enhanced U-Net network that incorporates multi-scale feature prediction fusion. Additionally, they introduced an improved parallel attention module to enhance the identification of concrete cracks. This multi-scale feature prediction fusion combines multiple U-Net features generated by intermediate layers for aggregated prediction, thus utilizing global information from different scales. The improved parallel attention module is used to process the U-Net decoded output of multi-scale feature prediction fusion, allowing it to assign more weight to the target region in the image and capture global contextual information to improve recognition accuracy. Similarly, [PERSON] et al. (2023) [12] focused on implementing a pixel-level convolutional neural network for detecting concrete surface damage in complex image settings. Given the presence of various types of damage on concrete surfaces, the convolutional neural network model was trained to identify three specific types of damage: Cracks,spalling, and voids. The training architecture utilized both the U-Net and DeepLabV3+ models, and a comparative evaluation was performed using various evaluation metrics to assess the predicted results. [PERSON] et al. (2016) [13] proposed a method for maintaining facilities by photographing the bridge inspection area using a drone and checking for cracks, efflorescence, or leaks using Context Capture, as the visual inspection of bridges using manpower and mounted vehicles is inefficient. Similarly, studies on facility maintenance using drones have shown that existing methods of bridge safety inspection and diagnostic surveys are inefficient. As an alternative, drones are being used. However, research on objectively classifying and detecting bridge damage based on the acquired data is lacking. [PERSON] et al. (2019) [14] noted that deep learning algorithms are open-sourced by global IT companies such as Google, making them readily available to anyone, and can be used by both industry and academia to solve a variety of problems across a range of industries, including civil engineering, manufacturing, steel, automotive, and infrastructure businesses.