# Texture Construction Edge Detection Algorithm
## 3 Experimental Results and Discussion
Figure 16e shows the edge image obtained by [PERSON] and [PERSON]'s method. The experimental results show that the detection effect on the texture of the tile is better than that in the above method, but only part of the texture on the tile and the edge of the line at the seam of the tile are detected, and most of the effective edge textures are still lost. Figure 16f shows the TCEDA proposed in this paper. Compared to the other four methods, the texture on the tile, the seam line between the tiles, and the wrinkles of the jeans can be effectively preserved. Figure 17 shows the results of comparing different methods in the Hakka Compound image. Figure 17a shows the original test image, where the tiles on the roof, the pattern on the wall with obvious light and shadow changes, and the distant skyline and antenna have obvious texture changes. Figure 17b shows the edge image obtained by the method proposed by [PERSON] and [PERSON]. The experimental results show that the edge detection results only preserve the edge contours of the roof and the wall periphery, and still have many effective edge textures missing. In Figure 17c-e, edge images are obtained from the experimental results by the methods proposed by [PERSON] et al., [PERSON] et al., and [PERSON] and [PERSON], respectively. It can be seen on the roof tiles that the edges of the lines and the pattern of the wall are better than the above methods, but the edge textures, such as significant light and shadow changes on the skyline, antennas, and walls, are still not effectively preserved. Figure 17f shows the TCEDA proposed in this paper. Compared to the other four methods, the tiles on the roof, the pattern on the wall with obvious light and shadow changes, and the distant skyline and antenna are remarkable, which shows that edge textures are effectively preserved. Figure 18 shows the results of a bridge image comparison using different methods. Figure 18a shows the original test image in which there are significant texture changes in the cable of the bridge, the railings on the bridge, and the cable supports next to the bridge. In Figure 18b-e, edge images  
are obtained by the methods proposed by [PERSON] and [PERSON], [PERSON] et al., [PERSON] et al., and [PERSON] and [PERSON], respectively. The results of the test only preserve the texture changes at the cable and railings, while the edge texture of the cable bracket cannot be effectively detected. Figure 18f shows the TCEDA proposed in this paper. Compared to the other four methods, significant edge textures, such as the bridge cable, railing on the bridge, and the cable bracket besides the bridge, can be effectively preserved and further verified. The TCEDA mentioned in this paper can detect richer edge textures. The edge detection results shown in Figures 14-18 effectively verify that the proposed TCEDA can detect the edge texture changes easily observed by the naked eye in the image and achieve richer edge detection results. The above four improved adaptive Canny edge detection algorithms use the statistics and distribution of pixel gradient values in the image to obtain the optimal threshold setting, but still lose many obvious edges in the experimental results. Therefore, the result of threshold setting directly affects the effect of edge detection. When the threshold is set too high, some edges will be lost. When the threshold is set too low, too much noise will be retained. Incorrect edge detection effects, such as incomplete object edges caused by missing edge textures or excessive false edges or noise interference, can cause erroneous results in subsequent object detection, identification, and segmentation processes. Therefore, it can be observed from the experimental results that the proposed TCEDA can construct the edge information of the object by retaining the effective edge texture change, which effectively improves the edge loss caused by unsuitable threshold setting. There are also few parameter values to be adjusted.