## 3 Experiment and Analysis  
This study examines six internally corroded API 5L X52 shallow offshore water injection pipeline segments, each approximately 12 m in length, with an outer diameter of 219 mm and a wall thickness of 12.7 mm. The feature tally of the six segments was obtained from two consecutive ILIs conducted four years apart using a UT intelligent pig [35]. The inspection setup, illustrated in Figure 15, depicts the direction of both inspections, from the manned wellhead platform to the unmanned wellhead platform. The length of the inspected pipeline is 1228 m. Inspection runs are completed in approximately 3.5 h at an average velocity of 352 m/h.  
Table 1 summarizes the number of features in sets P and Q, observed matches, and new features, as well as the complexity of ILI data across the six pipeline segments. Complexity  
Figure 14: Feature matching results obtained by the Voronoi model [10] using \(\delta\) = 0.110 m, \(\alpha\) = 0.020, and \(\tau\) = 0.001—example problem.  
Figure 13: Feature matching results obtained by the proposed model using \(\varepsilon_{\mathrm{x}}\) = 0.300 m, \(\varepsilon_{\mathrm{y}}\) = 0.150 m, \(\lambda\) = 0.250 m, \(\delta\) = 0.110 m, \(\alpha\) = 0.010, and \(\tau\) = 0.001—example problem.  
Figure 15: Illustration of the pipeline inspection setup from the manned wellhead platform to the unmanned wellhead platform.  
is categorized as low, medium, or high based on the density and distribution of defects along the segment and the severity of interactions between adjacent defects. Additionally, the two-dimensional (length and width) presentation of the features in set P and set Q for each pipeline segment is shown in Figure 16.  
Figure 16: Two-dimensional presentation (length and width) of all features across the six pipeline segments, S1 to S6—case study.  
This section presents a detailed analysis of the proposed model, focusing on sensitivity evaluations and comparisons. The analysis is structured as follows: First, a sensitivity analysis is performed on the Voronoi model's parameters to assess their influence on overall performance and establish a baseline for comparison, as outlined in Section 3.1. Similarly, Section 3.2 outlines the sensitivity analysis of the proposed model's parameters. The two models are then compared, focusing on their segment-level performance across varying ILI data complexities, as shown in Section 3.3. Finally, Section 3.4 presents the sensitivity analysis of the DBSCAN-based alternative model's parameters. The performance evaluation in this study consists of three key metrics, recall, precision, and F1 score, defined as follows [36]:  
* Recall assesses the model's ability to identify actual matches and new features, capturing the ratio of true positives among all relevant predictions (true positives and false negatives). High recall indicates the model is effective at finding most of the actual matches and new features.
* Precision focuses on the reliability of the model's positive predictions, representing the proportion of correctly identified matches and new features (true positives) among all predicted (true positives and false positives) matches and new features. High precision indicates fewer false predictions.
* F1 score provides a balanced measure of precision and recall, particularly useful in cases where these metrics need to be equally emphasized. It is defined as follows:  
\[\text{F1}=2\frac{\text{Recall}\times\text{Precision}}{\text{Precision}+\text{ Recall}} \tag{6}\]