Composite perception fusion detection framework for preoperational inspection of large-diameter pipelines

Abstract

Preoperational inspections of oil and gas pipelines are critical for ensuring their operational safety and integrity before commissioning. Given the complexities of pipeline environments and the wide range of potential defects, a comprehensive inspection methodology is essential. To address these challenges, we propose a novel composite perception fusion detection framework that offers comprehensive detection and localization of both environmental and defect anomalies through multisensor fusion. The proposed deep localization and classification decoupling (DLCD) network employed as the base detector simplifies the high-dimensional detection problem by decoupling the tasks of localization and classification, allowing for efficient defect detection with few-shot learning. The forward multispectral fusion detection system integrates infrared thermal testing (IRT) and visual testing (VT) to mitigate their respective limitations. Additionally, the incorporation of prior pipeline environment knowledge allows for efficient object-level registration of infrared and visible image pairs. The probability-based fusion strategy is employed to leverage the redundant information from both IR and visible modalities, significantly enhancing detection accuracy. Furthermore, by incorporating spatial relationships between forward and circumferential views, the circumferential defect detection system can efficiently detect weld defects based on the pipeline environment while achieving a 96.7% reduction in computational complexity. The proposed system is experimentally validated on a preoperational pipeline as well as a standard pipeline with artificial defects. Comparative experiments with state-of-the-art algorithms are performed to further verify the effectiveness and superiority of the framework.