## 5 Conclusions  
Realizing the benefits of collecting building information using visual data, researchers have begun to focus on the automatic extraction of structural characteristics from images to quickly assess seismic vulnerabilities of regional-scale buildings. However, existing structural element detection methods have a certain of limitations for the detection of school building columns and walls. To improve existing automatic structural element detection techniques, this paper combines the GLH statistical feature-based object-detection method with an improved shape-based method: the former can identify structural elements from complex scenarios, and the latter has proved its superior capability in detecting slant objects in an image. Focusing on detecting columns and walls, the proposed method was validated by testing the detection precision and recall for column and wall images selected from the database of NCREE, respectively. The main advantages of the proposed column and wall detection method are listed as follows. First, the present method is able to detect columns and walls with various surface treatments or slightly tilting shapes which frequently occur in images. Second, images in different sizes and resolutions taken by either cameras or mobile phones can be used in this method, broadening the sources and decreasing the cost for images collection. Last, the proposed method is insensitive to illumination intensities which means that images taken in different time periods of a day  
Figure 7: The wall detection result of the No. 13 image in Table 2 (sub-region 2, 4, and 6 contain walls while only walls in sub-region 2 and 6 are detected): (**a**) the preprocessed image; (**b**) variance of GLHs; (**c**) skewness of GLHs; (**d**) kurtosis of GLHs; (**e**) the GLH in sub-region 2 (a wall); (**f**) the GLH in sub-region 6 (a wall).  
can be used in this method. As three GLH statistical parameters used in this method only depend on the shapes of GLHs, their values are insusceptible of the average high or low illumination intensities. On the other hand, as for the unsuccessful detection of columns and walls, the main reason is that images are not taken from the front view of structural elements. Camera tilting and perspective illusion can aggravate the inclination of distant elements, leading to the failure of detecting their boundary lines. Therefore, taking images from the front view of the target structural elements are recommended.  
Although the proposed method was applied here to only detecting and counting columns and walls, it can be further extended to detect other structural characteristics, such as sizes and surface defects of structural elements, or structural deficiencies, such as short columns and soft-stories, as broader applications to automatically extract structural information from digital images. Furthermore, as glass has been widely used for facades in modern buildings and the failure of such glass-elements can lead to serious casualties in earthquakes, automatic detection of building elements made of glass is a critical and challenging issue in the future [52]. The proposed GLH statistical feature-based object-detection method has the potential for solving such issue because the gray levels of glass-elements' surfaces are homogeneous compared with the gray levels of surrounding environment that contains various objects, while the color/texture-based methods may fail to work in detecting glass that has various colors and surface treatments.  
Conceptualization, Z.Z. and H.-H.W.; Methodology, Z.Z. and H.-H.W.; Validation, Z.Z. and H.-H.W.; Resources, Z.Z. and J.-H.C. Writing--original draft preparation, Z.Z.; writing--review and editing, H.-H.W., S.G.Y., and J.-H.C.  
This research received no external funding.  
The authors declare no conflict of interest.