【摘要】 为解决显微图像中小尺寸、低对比度及复杂背景下的缺陷检测难题，提出一种基于层次化显微特征建模与仿真的缺陷检测技术。该方法以RT-DETR(real-time detection transformer)框架为基础，构建HM-RTDETR(hierarchical microscopic RT-DETR)模型，在保持Transformer全局特征建模能力的基础上，引入高密度一对一Mosaic(Dense O2O-Mosaic)仿真增强以提升小样本监督密度，设计深度可分离卷积(depthwise separable convolution,DWConv)局部细节增强模块强化显微纹理特征提取能力，并采用可学习上采样模块(PatchExpand)实现空间语义重建，从而增强模型对微小缺陷的辨识能力。进一步构建DWConv与PatchExpand的协同融合结构，实现多尺度特征自适应整合与轻量化优化。实验结果表明，所提模型在两组离线增强机制下均保持优势：HM-RTDETR的mAP0.5-0.95在设置Ⅰ与设置Ⅱ下分别达到57.8%与70.4%，较对应基线分别提升18.9%和21.6%。在精度与实时性之间实现良好平衡，为金相显微组织自动检测提供了一种高效、可推广的解决方案。更多还原

【Abstract】 To address the challenge of detecting small and low-contrast defects in complex microscopic images, a defect method technology based on hierarchical microscopic feature modeling and simulation is proposed. The method is built on the RT-DETR(real-time detection transformer)framework to construct the HM-RTDETR(hierarchical microscopic RT-DETR) model. It maintains the global feature modeling ability of the Transformer and introduces a Dense O2O-Mosaic, a high-density one-to-one Mosaic augmentation strategy, to increase supervision density for small samples. A depthwise separable convolution(DWConv) module is used to enhance local detail extraction in microscopic textures, and a learnable PatchExpand module is applied for spatial semantic reconstruction, improving the identification of tiny defects. Furthermore, the cooperative fusion of DWConv and PatchExpand achieves adaptive multi-scale feature integration and lightweight optimization.Experimental results show that the proposed model maintains consistent advantages under both offline augmentation settings: HM-RTDETR achieves mAP0.5-0.95 of 57.8% and 70.4% under Setting Ⅰ and Setting Ⅱ, respectively, improving over the corresponding baselines by 18.9% and 21.6%.The method achieves a good balance between accuracy and efficiency, providing an effective and extensible solution for automatic microscopic defect detection and simulation analysis.更多还原