计算机工程与应用2025，Vol.61Issue(15)：132-143,12.DOI:10.3778/j.issn.1002-8331.2502-0222
MMF-YOLO晶圆模具表面微缺陷检测算法
MMF-YOLO Algorithm for Detection of Micro-Defects on Wafer Mold Surfaces
摘要
Abstract
To address challenges such as small target size,significant scale variation,complex backgrounds,and low detection accuracy in micro-defect detection on wafer mold surfaces,this paper proposes the MMF-YOLO algorithm,which combines edge information focusing and context information fusion diffusion.Firstly,the edge information focus-ing module(EIFM)is introduced to enhance the C3k2 module in the original network,enabling the selection of key features highly correlated with the target from multi-scale edge information.Secondly,the context-fusion diffusion pyramid net-work(CFD-PN)structure is employed to optimize the neck network,reducing feature confusion and loss during informa-tion fusion by extracting multi-level representations of features in terms of spatial resolution and semantic information.Additionally,the adaptive down-sampling module(ADown)is incorporated to optimize the number of parameters and computational redundancy in convolutional layers,thereby reducing model complexity.Finally,the feature scale-aware detection head(FSDH)is utilized to minimize network storage overhead by employing shared convolutions.Experimental results demonstrate that the MMF-YOLO algorithm achieves a 6.93 percentage points improvement in mAP@0.5 com-pared to the baseline YOLOv11n on the wafer mold surface micro-defect dataset,making it more suitable for micro-defect detection tasks on wafer mold surfaces and deployment on embedded platforms for efficient inference.关键词
机器视觉/微缺陷检测/边缘信息增强/上下文融合扩散金字塔/YOLOv11Key words
machine vision/micro-defect detection/edge information enhancement/context-fusion diffusion pyramid/YOLOv11分类
信息技术与安全科学引用本文复制引用
冯金秋,燕芳,杨阳,李海宇..MMF-YOLO晶圆模具表面微缺陷检测算法[J].计算机工程与应用,2025,61(15):132-143,12.基金项目
国家自然科学基金(62161042) （62161042）
内蒙古自治区关键技术攻关计划项目(2021GG0361). （2021GG0361）
