Article · Technologies · November 6, 2026
MFL-ScrollNet: Recurrent Sliding-Window Detection for Panoramic Pipeline Magnetic Flux Leakage Images
Aleksei Samarin, Fyodor Belous, Aleksei Toropov, Egor Kotenko, Artem Nazarenko, Dmitry Nazarenko
Abstract
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.
More by Valentin Malykh
- PACTraD: Multi-Task Benchmark and Domain-Specific Attention Mechanism for Wildlife Classification, Detection, and Segmentation in Protected Area Camera Trap DataBig Data and Cognitive Computing · 2026
- Beyond Attention: Module-Level Refusal Directions in Mamba-Based Hybrid Language ModelsLecture notes in computer science · 2026
- A Reproducible Microscopy Acquisition Pipeline and Microscopy-Aware Attention for Classification, Detection, and Segmentation of MicroorganismsMachine Learning and Knowledge Extraction · 2026
- Hybrid Non-local Blocks for Streptococci Segmentation in Microscopic ImagesLecture notes in networks and systems · 2026