# Automatic Extraction of High-Voltage Power Transmission Objects from UAV Lidar Point Clouds
## 4 Experiments and Analysis
### Results and Analysis
#### 4.3.3 Optimization  
The results of pylon detection according to the optimization algorithm described in Section 3.4 are given in Figure 15d,f, As a matter of fact, individual tall trees and signal poles had the same spatial distribution as the HV tower, in terms not only of the large height difference and continuous elevation distribution, but also the local maximum of point density. In addition, other linear objects, e.g., low-voltage transmission wires and crossover lines, which were not objects of interest, were also detected. Such objects had similar characteristics of distribution and local linearity as the HV transmission lines. The final pylon results, after the connectivity analysis between the PLs and the adjacent two pylons, are shown in Figure 16a,b. The refined PL results are shown in Figure 16c. Compared with the previous results, several boundary lines of high-rise buildings and low-voltage transmission towers and lines were removed. The result is shown in Figure 16d. After the refinement, according to the spatial relations of the extracted power objects, only the complete HV power elements with spatial connectivity were retained. It can be seen in Figure 16e that the low-voltage PL segments were removed. The result in Figure 16 showed an experimental result by [PERSON] [69] in a similar scenario, in which low-voltage PLs are also retained. Figure 15: Tower extraction: (**a**) schematic map of the original data without PLs; (**b**) the result of the big difference grid in Dataset 1; (**c**) grid clustering by height of the data in (**b**); (**d**) the refined results of the dataset in (**b**); (**e**) intermediate result of Dataset 2; (**f**) the refined results of Dataset 2. **The accuracy of the extracted power line:** The precision and recall of the power line points were used to evaluate the results, as in Equations (8)-(10):  
\[\text{Precision }=\frac{\text{TP}}{\text{TP}+\text{FP}}\;, \tag{8}\]  
\[\text{Recall }=\frac{\text{TP}}{\text{TP}+\text{FN}^{\prime}}\;, \tag{9}\]  
\[\text{F1}=2/\Big{(}\frac{1}{\text{Precision }}+\frac{1}{\text{Recall }}\Big{)}\;. \tag{10}\]  
Figure 16: Refinement of the results: (**a**) the reduced tower point cloud; (**b**,**c**) the refined result of transmission elements in the cross-over area; (**d**) reconstructed transmission lines from Dataset 2; (**e**) reconstruction result within a span; (**f**) reconstruction result in Reference [69] (containing low-voltage PLs). where TP (true positive) is the number of PL points that were correctly labeled, FP (false positive) is the number of non-PL points incorrectly detected as PL points, and FN (false negative) denotes the number of PL points that were falsely labeled as other objects. The performance of the extraction is presented in Table 4; the precision and recall were greater than 96% on average. The F1 scores were 0.956 and 0.973 for Dataset 1 and Dataset 2, respectively. The mislabeled PL points were mostly from a low-voltage crossing-over line, of which the vertical distance was very close. To quantitatively validate the performance, we tried to realize the method proposed by [PERSON] [31] and tested it with our data. The result of Dataset 1 showed a higher precision, but a lower recall rate and a similar F1 score. For Dataset 2, the proposed method had a slightly better result. The comparison is detailed in Table 5.  
**The accuracy of extracted pylons:** The pylon detection of the pylon was also evaluated by precision, recall, and F1, as shown in Table 6. There were 41 and 26 pylons in total in Datasets 1 and 2, respectively. However, there was a low-voltage pylon in Dataset 1. The precision of extracted pylons in the two datasets was 93% and 100% respectively. The number of towers detected in the two datasets was 38 out of 41, and 26 out of 26, respectively. Dataset 2 had a higher density, hence higher accuracy. Dataset 1 had relatively large topographic relief, with a small number of tall forest trees.