DAMF-Net: A Dynamic Receptive Field Enhancement and Semantic-Guided Adaptive Modulated Fusion Network for Steel Surface Defect Segmentation.

In the industrial strip steel production process, complex background interference and varying defect scales make existing semantic segmentation models prone to scale confusion and noise propagation issues during the feature representation stage, thereby limiting segmentation accuracy and stability. To address these problems, this paper proposes a steel surface defect segmentation network based on dynamic receptive field modulation and semantic-guided adaptive modulated fusion, termed DAMF-Net. First, in the encoding stage, a kernel selection fusion attention module (KSFA) is designed, which constructs multi-scale receptive field responses through cascaded depthwise convolution, and combines spatial average prior with channel-level competitive weights to perform adaptive gating modulation on bottleneck features, thereby enhancing the model's ability to represent different scale defect structures; second, in the decoding stage, a task-oriented Adaptive Modulated Fusion Module (AMFM), inspired by modulation-based feature fusion, is introduced to adaptively fuse shallow detail features and deep semantic features through branch-wise competitive weighting for each channel, thereby reducing the propagation of shallow background noise during decoding; additionally, a hybrid optimization objective combining binary cross-entropy and Dice loss is constructed to enhance the model's learning ability for small-scale and low-contrast defects. Experimental results on the Severstal and ESDIs-SOD datasets show that the proposed method improves the mDice/Dice metrics by 2.38% and 4.40% respectively compared to the U-Net baseline, and exhibits lower prediction errors and more stable segmentation performance under complex background conditions. Meanwhile, DAMF-Net achieves an inference speed of 55.8 FPS while ensuring improved accuracy, demonstrating a good balance between precision and efficiency. This method provides an effective solution for high-precision segmentation of steel surface defects in complex industrial scenarios.