# Research on Damage Localization of Steel Truss-Concrete Composite Beam Based on Digital Orthoimage
## 1 Introduction  
With the rapid development of Computer Science and Technology, the difficulty of identifying structural damage caused by incomplete test data can be effectively alleviated by the ability of images to completely describe the structure morphology. Non-contact data collection through digital images has the advantages of being holographic, convenient, and economical compared to traditional point sensor-based monitoring methods. In recent years, with the development of hardware conditions such as pre-installed camera, UAVs, and wearable virtual reality equipment, image archives of structural damage can be created based on accumulated monitoring data and previous inspection results. Therefore, vision-based damage detection and identification techniques are more easily applied to real structures, offering the possibility of identifying structural damage from images. Computer vision and image processing techniques have been widely used for damage identification on image datasets such as concrete cracks [1], concrete spalling [2], pavement cracks [3], underground concrete pipe cracks [4], and asphalt pavement potholes [5]. For the detectionof local cracks in structures, image processing techniques meet the need for intuitive and fast detection [6; 7]. [PERSON] et al. [8] conducted a study on the automated detection of cracks in concrete bridges. The images are first filtered and smoothed and noise is removed using increasing structural elements with alternating opening and closing; then, the edges of the bridge cracks are accurately extracted using a multi-scale morphological edge detector, and the development of the cracks is tracked and localized. [PERSON] et al. [9] have developed a vision-based image acquisition robot and subsequently proposed a more advanced automatic vision monitoring system. The system uses a vision-based remote-controlled robot for image acquisition and digital image processing software to identify the crack size in the captured images, making the inspection process faster, safer, more reliable, and less costly. [PERSON] et al. [10] proposed a vision-based bridge crack detection technique by automatically processing target detection and grouping. [PERSON] and [PERSON] et al. [11; 12] combine a low-rank approach with a sparse representation to detect local structural damage in real time using video. The monitoring of various complex, hidden, and high-altitude parts of bridges has been achieved by using intelligent robots and drones instead of the traditional manual safety monitoring methods [13]. The use of camera-equipped unmanned aerial vehicles (UAVs) for bridge safety condition monitoring is growing exponentially [14]. At present, with the improvements of monitoring equipment, lightweight, miniaturized, and accurate monitoring devices of various types can be mounted on UAVs. Therefore, new intelligent bridge monitoring technology based on the UAV monitoring platform has become a hot field of current research and technical application. [PERSON] et al. [15] developed a novel system framework for bridge inspection and management: i.e., images are collected by camera-mounted UAS, inspection data are collected and processed based on computer vision algorithms, and a bridge information model (BrIM) is used to store and manage all relevant information. [PERSON] et al. [16] use camera-equipped UAVs to collect high-definition image data of bridge structures; the flight paths are automatically calculated by 3D models, and the intelligent safety assessment of large bridges is achieved by using the machine learning-based identification of typical damage patterns. [PERSON] et al. [17] use a UAV and three-points laser rangefinder to collect images of bridge structures and construct a training model of intelligent crack morphology extraction based on the Support Vector Machine (SVM) to realize the intelligent recognition of bridge crack width. [PERSON] et al.