## 4 Implementation and Results
### Implementation and Discussion
#### 4.2.2 Wall detection results and discussion  
As shown in Table 2, similar to column detection results, the detection precision of all 20 wall images are 100% and the detection recall ranges from 50% to 100% with the average of 73.2%, proving the outstanding capability of the proposed method in correctly detecting columns and walls. Looking into these images, the authors found that the proposed method was able to detect structural elements in complex scenarios. Take the No. 1 image in Table 2 as an example. Compared with the similar GLHs among non-column sub-regions (Figure 3), GLHs are markedly different from each other among non-column sub-regions in Figure 5a, leading to the huge differences of variance, skewness and kurtosis values among non-column sub-regions. The variance value in the sub-region 2 is much smaller than the value in the sub-region 6 (Figure 5b,c) while the skewness value in the sub-region 2 is much bigger than the value in the sub-region 6 (Figure 5d). However, most sub-regions with complex scenarios in wall images have little influence on the detection results because only these sub-regions adjacent to structural element sub-regions can determine the detection results of structural elements. As shown in Figure 5, whether the wall in sub-region 4 could be successfully detected is only affected by adjacent sub-region 3 and 5 in spite of other sub-regions with diverse GLHs. Moreover, the proposed detection method is able to detect slightly distorted structural elements by setting an allowed intersection angles (5\({}^{\circ}\)) of objects' two vertical boundary lines, compared with existing structural element detection methods which assume that the vertical boundaries of a structural element are a pair of parallel lines [28]. For instance, the wall (sub-region 4) in Figure 5a were successfully detected even if its two boundary lines are not parallel with an intersection angle of 2\({}^{\circ}\) caused by perspective illusion. On the other hand, the perspective illusion can lead to the failure detection of distant structure elements in images whose boundary lines are too blurry to be detected. For instance, wall 1 in Figure 6a and wall 1 in Figure 6b were not successfully detected because the edge detection method was unable to retrieve these walls' left boundary lines that are invisible due to far distance between the walls and the camera-perspective illusion makes distant object small and blurry. Therefore, images that are taken from the side view of the structural elements to be detected result in low detection recall. Figure 4: Examples of column detection results: (**a**) the No. 3 image in Table 1; (**b**) the No. 16 image in Table 1.  
Another advantage of the presented method is that different illumination intensities in images will not hinder structural elements from being successfully detected. The change in illumination intensities can only result in translation motion of GLHs: the peaks of GLHs move to high gray level side under high illumination and move to low gray level side under low illumination, without changing their concentration, symmetry, and tailedness. As shown in Figure 7, gray levels of pixels in light sub-region 2 mainly range from 200 to 250 while gray levels of pixels in dark sub-region 6 are mainly around 150. However, the shapes of these two GLHs show fewer differences. The insensitivity to illumination intensity of this method allows images to be collected throughout the daytime, overcoming the limitation of traditional image recognition methods that images should be collected in the same time period of a day ([PERSON] et al. 2017). On the other hand, the non-uniform illumination on the surface of an element can result in failure of detection due to the asymmetric distribution of the element's GLH. For instance, the wall 4 in Figure 7a, with non-uniform illumination on its surface, failed to be detected because of the local maximum value of the skewness in sub-region 4 (Figure 7c).