PixTransNet: A Sensor-Aware CNN-Transformer Model for Magnetic Flux Leakage Defect Segmentation

Magnetic Flux Leakage (MFL) is a widely used non-destructive evaluation (NDE) technique for pipeline inspection. However, its signals are highly sensitive to noise and geometric distortions, causing small defects with limited spatial coverage and subtle defects with low-contrast patterns to be embedded in noise, resulting in indistinct boundaries and irregular shapes that complicate segmentation. To address these challenges, we propose PixTransNet, a hybrid CNN–Transformer model built on a UNet encoder–decoder architecture with a ResNet18 backbone, designed to improve the segmentation and boundary localization of small and subtle defects in MFL signals. We embed pixel-aware transformer blocks into the deeper encoder stages to capture long-range dependencies and enhance the modeling of subtle and fragmented defect patterns. To further enhance the interpretation of MFL signals, we introduce a cross-attention module that selectively emphasizes signal regions with strong structural relevance, leading to more continuous and accurate defect boundaries, particularly for small defects. Extensive experiments on a large-scale dataset of 33,000 MFL images demonstrate that PixTransNet achieves notable improvements in segmentation quality, particularly in detecting small, weak, and low-contrast defects compared to existing baselines. PixTransNet achieves 48.30% IoU, representing a 1.97% improvement, and 70.73% Recall, representing a 13.06% improvement over the best-performing baseline