Toward Image–Magnetic Flux Leakage Dual-Modality Inspection of Steel Pipes and Curved Steel Surfaces: Evidence, Fusion Strategies, and Robotic Acquisition

Steel pipes and curved steel plates in the petroleum, chemical, and energy industries are prone to surface and weld defects that threaten structural integrity. Single-modality inspection by machine vision or magnetic flux leakage (MFL) struggles under real production conditions, including uneven illumination, occlusion, and electromagnetic noise. This review examines how far published evidence (2016–2026) supports combined image–MFL inspection, organizing 126 works into four themes (vision, MFL, fusion, and robotic platforms) and positioning them against fifteen prior surveys. Reported results, synthesized by dataset and evaluation protocol, show that vision detectors reach 82–83% [email protected] on public benchmarks and up to 97.9% on self-built datasets, strict-IoU scores fall to ≈47% within a single study, and MFL baselines on the large-scale public PipeMFL-240K benchmark reach [email protected] =0.498. Fusion evidence is graded by comparison design. Data-, feature-, and decision-level fusion schemes are compared; bidirectional cross-modal guidance is proposed for future testing. Cross-device registration, paired-data scarcity, quantification accuracy, and inconsistent evaluation are the main obstacles; physics-informed Transformer fusion and joint sensor–robot design are promising directions.