DFI-Net: dual-branch feature interaction network with attention mechanism for metal workpiece surface defect segmentation

Abstract In contemporary industrial manufacturing, the automatic detection of surface defects on metal workpieces is a crucial factor for improving product quality and enhancing production efficiency. Therefore, we introduce a novel dual-branch feature interaction network equipped with two advanced attention mechanism specifically designed for defect segmentation, named DFI-Net. Specifically, we adopt an encoder–decoder framework to facilitate effective feature learning for image processing tasks. To enrich feature interaction and improve representation capability, we build a dual-branch architecture that enables complementary information exchange between parallel pathways. Within the encoder stage, we design a bottleneck dilated convolution module and a depthwise-pooling feature mixer module to expand the receptive field and extract multi-scale contextual features. Subsequently, we integrate two specialized modules in the transition layer of the encoder and decoder. The first is a multi-dilation soft attention fusion module, which aggregates features obtained at different dilation rates and dynamically reweights them through soft attention mechanisms. The second is a large kernel attention module, which captures long-range dependencies and broader spatial patterns using expanded convolutional kernels. Finally, the proposed DFI-Net was rigorously evaluated using both the publicly available SD-saliency-900 dataset and a proprietary Bearing surface defect dataset collected from actual industrial scenarios. On the SD-saliency-900 dataset, DFI-Net achieved Dice of 0.8860, Mcc of 0.8701, and Jaccard of 0.7976. Meanwhile, on the Bearing dataset, DFI-Net obtained Dice of 0.7873, Mcc of 0.7884, and Jaccard of 0.6571. Furthermore, on the public road crack dataset DeepCrack, the DFI-Net achieved Dice of 0.8645, Mcc of 0.8612, and Jaccard of 0.7668. Experimental results across these datasets consistently demonstrated that DFI-Net exhibits high segmentation precision, strong robustness, and excellent adaptability to different defect types. In addition, comprehensive ablation studies were carried out to systematically assess the individual contributions of each component within the proposed network architecture.