Semantic segmentation method for micro-cracks in silicon nitride ceramic bearing balls based on coupling of edge channel enhancement and weighted gated attention mechanism in EMU-Net+

Abstract

In addressing the issues of blurred microcrack edge gradients and mutual interference among various types of microcracks in the semantic segmentation of microcracks in silicon nitride bearing balls. Based on coupling edge channel enhancement and a weighted gated attention mechanism within the EMU-Net + framework method is proposed. The Unet + function matrix is constructed with long-short connection structures, incorporating feature fusion across different layers to obtain a larger receptive field. The edge channel enhancement and weighted gated attention mechanism are established, feeding back features segmented at different scales to the higher level, enhancing the contrast between features and the background, achieving finer segmentation of microcrack mask images. Compared to the baseline Unet model, segmentation metrics including Miou, Mpa, Precision, and Recall on the microcrack dataset are improved by 6.73 %, 3.1 %, 5 %, and 3.41 %, respectively. The average intersection and merger ratios of three different microcrack types all exceed 80 %.