Title: Multi-Modal Pipeline Defect Localization
Authors: Mariam Manzoor, Zahra Arabi Narei, Henry Leung, Scott Miller
Abstract: This study investigates the use of Laser and Magnetic Flux Leakage (MFL) pipeline data to develop a deep learning model for accurate detection and segmentation of pipeline defects. Laser images are used to precisely identify defect regions and provide labels for training a Mask R-CNN model for localizing and segmenting defects in MFL signals. Unlike conventional datasets where ground-truth labels are pixel-wise accurate, our labels are derived from a different sensor modality, resulting in misalignment and feature discrepancies between the laser and MFL data. These discrepancies lead to label noise and domain shift. Our experiments show that training advanced object detection and segmentation models using only laser-derived labels does not achieve accurate defect localization in MFL signals. This underscores the need for models capable of handling label discrepancies and adapting across domains to ensure robust and scalable performance in real-world pipeline defect detection.
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