MFL Image Recognition Method of Pipeline Corrosion Defects Based on Multilayer Feature Fusion Multiscale GhostNet

A method based on multilayer feature fusion multiscale GhostNet (MFMSGN) is proposed to improve the accuracy of magnetic flux leakage (MFL) image recognition of pipeline corrosion defects. First, a multiscale ghost module is constructed to make it more suitable for practical applications, which can obtain a large number of multiscale features through a small amount of calculation. Then a parallel structure is used instead of the traditional method of improving accuracy by deepening the model depth. In this paper, we introduce an adaptive spatial feature fusion (ASFF) method to fuse features of different resolutions to widen the width of the network and apply it to downstream classification tasks. Finally, a lightweight and efficient model is proposed. The results of the experiment showed that the accuracy of the eight-layer MFMSGN for MFL corrosion defect identification is better than ResNet50 and slightly inferior to ResNet101, but the computational effort is much smaller.