计算机工程与应用2026，Vol.62Issue(15)：159-169,11.DOI:10.3778/j.issn.1002-8331.2507-0341
轻量动态融合Transformer晶圆模具微缺陷检测(LDF-DETR)
Lightweight Dynamic Fusion Transformer for Wafer Mold Micro-Defect Detection(LDF-DETR)
摘要
Abstract
To address the challenges of low signal-to-noise ratio,severe pseudo-defect interference,limited detection accuracy,and high industrial deployment complexity in the detection of micro-defects on wafer mold surfaces,this paper proposes a lightweight dynamic fusion detection model,LDF-DETR.This method is an enhancement of the RT-DETR architecture,where a dynamic alignment fusion(DAF)module is embedded in the backbone network to enable spatial semantic adapta-tion and parametric feature reorganization across multi-branch features.Innovatively,a learnable position encoding mecha-nism is introduced,which optimizes the potential spatial distribution of position embedding vectors through gradient feed-back,thereby improving the model's ability to perceive microscopic defect location features.To enhance computational efficiency,a dynamic group convolution with channel shuffle mechanism is proposed,effectively reducing computational redundancy by integrating channel reallocation.Experiments conducted on an industrial-grade micro-defect dataset for wafer molds demonstrate that LDF-DETR achieves a simultaneous reduction of 34.3 GFLOPs in computational cost and 8.38×106 parameters,while maintaining a mAP@0.5 detection accuracy of 93.72%,significantly outperforming existing light-weight object detection models.关键词
机器视觉/微缺陷检测/可学习位置编码/动态分组卷积混洗/RT-DETR架构Key words
machine vision/micro-defect detection/learnable position encoding/dynamic group convolution shuffle/RT-DETR architecture分类
信息技术与安全科学引用本文复制引用
冯金秋,燕芳,李小娜,杨阳,李海宇..轻量动态融合Transformer晶圆模具微缺陷检测(LDF-DETR)[J].计算机工程与应用,2026,62(15):159-169,11.基金项目
国家自然科学基金(62161042) （62161042）
内蒙古自然科学基金(2025MS06002) （2025MS06002）
内蒙古重点研发和成果转化计划项目(2025SYFHH0875). （2025SYFHH0875）
