MFL-ScrollNet: Recurrent Sliding-Window Detection for Panoramic Pipeline Magnetic Flux Leakage Images

Automated interpretation of magnetic flux leakage (MFL) data is central to condition assessment of long-distance oil and gas pipelines. Yet, full-resolution panoramas are difficult to process without either suppressing small anomalies through resizing or fragmenting elongated objects at sliding-window boundaries. We propose MFL-ScrollNet, a recurrent sliding-window detector that formulates panoramic analysis as an ordered sequence problem. A gated recurrent unit propagates a compact hidden state across narrow axial windows, while geometric cross-window association in global panorama coordinates, complemented by learned novelty prediction, consolidates detections across adjacent windows. Experiments on the public PipeMFL-240K benchmark show that the full model attains an mAP50 of 0.535, an mAP50:95 of 0.332, a macro-averaged F1 score of 0.662, and a micro-averaged F1 score of 0.547. MFL-ScrollNet achieves competitive average-precision performance, surpassing the strongest reproduced static baseline, DINO-R50, while approaching the performance of the domain-specific STPDNet. At the selected operating point, MFL-ScrollNet achieves the highest macro- and micro-averaged F1 scores among the evaluated methods, demonstrating improved detection performance across both individual classes and the overall defect population. Controlled ablations show consistent gains from recurrent context, global-coordinate association, and learned novelty prediction. These results demonstrate that sequential cross-window reasoning improves both class-balanced and instance-level detection in panoramic MFL data.