WF-MobileNet: A Lightweight Wavelet–Fusion Network for Classifying Defects in 3D Surface Morphology Images of Seamless Steel Pipes

Seamless steel pipes require reliable classification of outer-surface anomalies, yet class imbalance, scale variation, and visual overlap between defects and production interference complicate automated inspection. We propose WF-MobileNet for classifying two-dimensional RGB pseudo-colour surface morphology images derived from line-structured-light profiling. The model retains MobileNetV3-Small as its primary descriptor path, applies Selective WTConv to selected middle- and late-stage depthwise operations, and aggregates features at three spatial resolutions through Lite-BiFPN. A gated residual connection adds the multiscale descriptor to the terminal backbone descriptor. Evaluation used 15,463 images covering 12 defect classes and 4 production interference classes, with five training seeds on one fixed partition. WF-MobileNet achieved the highest observed mean macro-F1 and interference F1 among the six evaluated architectures, reaching (82.17 ± 0.36)% and (80.50 ± 0.96)%, respectively (mean ± sample standard deviation). The corresponding gains over MobileNetV3-Small were 4.20 and 6.84 percentage points. With 1.811 million parameters and 65.8 million multiply–accumulate operations (MACs), WF-MobileNet ranked second lowest on both complexity measures. Controlled ablation revealed larger mean macro-F1 gains from the joint configuration than from either component alone. Within the evaluated archive, WF-MobileNet improved class-balanced recognition and defect–interference discrimination while retaining low parameter and MAC counts.