###### Abstract  
Corrosion is considered a leading cause of failure in pipeline systems. Therefore, frequent inspection and monitoring are essential to maintain structural integrity. Feature matching based on in-line inspections (ILIs) aligns corrosion data across inspections, facilitating the observation of corrosion progression. Nonetheless, the uncertainties of inspection tools and corrosion processes present in ILI data influence feature matching accuracy. This study proposes a new extensible feature matching model based on consecutive ILIs and data clustering. By dynamically segmenting the data into spatially localized clusters, this framework enables feature matching of isolated pairs and merging defects, as well as facilitating more precise localized transformations. Moreover, a new clustering technique--directional epsilon neighborhood clustering (DENC)--is proposed. DENC utilizes spatial graph structures and directional proximity thresholds to address the directional variability in ILI data while effectively identifying outliers. The model is evaluated on six pipeline segments with varying ILI data complexities, achieving high recall and precision of 91.5% and 98.0%, respectively. In comparison to exclusively point matching models, this work demonstrates significant improvements in terms of accuracy, stability, and managing the spatial variability and interactions of adjacent defects. These advancements establish a new framework for automated feature matching and contribute to enhanced pipeline integrity management. p 2025 15 2943  
thereby leading to additional material loss and wall thinning. This escalation increases the pipeline's vulnerability to high-pressure conditions, potentially compromising its structural integrity and elevating the likelihood of leaks or ruptures. Such failures can result in substantial financial losses, alongside severe environmental and safety repercussions. The economic impact extends beyond direct repair costs, affecting operational efficiency and necessitating unplanned downtime, which can disrupt supply chains and regulatory compliance [3,7,8]. Figure 1 shows various internal corrosion defects on fragments of a pipe wall [9], demonstrating examples of material loss and wall thinning. Regardless of the technology used, ILI tools face inherent limitations due to detection and measurement uncertainties. These tools detect and measure corrosion defects larger than a defined detection threshold, commonly 10% of the wall thickness for MFL-based tools. Additionally, a reporting threshold may also be applied, where only defects with a size exceeding this threshold are reported, thereby reducing false positives often associated with smaller corrosion defects. Despite these thresholds, detection errors may still occur, even for defects above the detection threshold, as ILI tools cannot guarantee complete accuracy. This is quantified by the probability of detection (POD), which describes the likelihood that a defect will be detected when it exceeds the detection threshold. Conversely, false call errors can arise when the tool identifies a defect that does not exist, with the probability of false alarm (POA) describing the likelihood of such occurrences. In addition to that, ILI tools are also subjected to directional location uncertainties due to limitations in their sensor and coordinate systems. These location uncertainties can be seen as a calibrating uncertainty of the defect's position (\(\delta\)) [10, 11, 12, 13, 14, 15]. The raw data captured by ILI tools, which represent corrosion defects in the form of signals, are processed into a feature tally. Corrosion defects are reported in the tally as features with lengths, widths, and depths determined by the signal's peak in each dimension, while their location is reported in terms of axial position and circumferential (12 h clock orientation) or angular position.