针对单可见光或单红外条件下的IC器件表面缺陷对比度不足,缺陷检测精度低的问题,提出多光谱图像融合的IC器件表面缺陷检测方法.针对IC器件可见光与红外图像配准中存在尺度不一致和对比度反转问题,引入拉普拉斯金字塔和特征描述符重组策略改进ORB(Oriented FAST and Rotated BRIEF)图像配准算法.在图像配准的基础上,提出NSST_VP图像融合方法,以非下采样剪切波变换(Non-Subsample Shearlet Transform,NSST)得到红外图像和已配准可见光图像的低频和高频子带,对低频子带采用视觉显著图(Visual Significance Map,VSM)加权融合规则,高频子带则采用自适应脉冲耦合神经网络(PA-Pulse Coupled Neural Network,PA-PCNN)决策融合规则,进而通过NSST逆变换得到高质量多光谱融合图像.最后,将融合图像输入YOLOv8s模型进行检测.实验结果表明,改进ORB的图像配准平均精度为87.8%,比ORB图像配准精度提高了62%,NSST_VP图像融合算法在主观视觉效果和客观评价指标上均有所提高.在缺陷检测实验中,NSST_VP融合方法的均值平均精度(mean Average Precision,mAP)达到83.15%,比单可见光、单红外缺陷图像检测的mAP分别提高了22.97%,28.31%,比双树复小波变换融合、曲线变换融合、非下采样轮廓波变换融合方法的mAP分别提高了13.14%,15.01%,20.35%.
Multispectral image fusion method for surface defect detection of IC devices
To address the issue of low defect detection accuracy in IC devices due to insufficient contrast under either visible light or infrared conditions alone,this paper introduces a multi-spectral fusion ap-proach.Initially,to overcome scale inconsistency and contrast inversion challenges during IC device image registration,we enhance the ORB(Oriented FAST and Rotated BRIEF)algorithm with a Laplacian pyra-mid and feature descriptor recombination strategy.Following image registration,we propose the NSST_VP image fusion method,which processes the infrared and visible images'low and high frequency subbands through Non-Subsample Shearlet Transform(NSST).For fusion,the low frequency subband us-es a visual significance map(VSM)weighted rule,and the high frequency subband employs a PA-Pulse Coupled Neural Network(PA-PCNN)decision rule,with the final image produced by reversing the NSST.The fused image is then analyzed using the YOLOv8s model.Experimental findings reveal an 87.8%average accuracy with the improved ORB registration,marking a 62%enhancement over the stan-dard ORB.The NSST_VP fusion algorithm significantly boosts both subjective and objective metrics,achieving an mAP of 83.15%-surpassing single light mode detections by 22.97%and 28.31%,and outper-forming Dual-Tree Complex Wavelet,Non-Subsampled Contourlet,and Curvelet Transform fusion meth-ods by 13.14%,15.01%,and 20.35%,respectively.
万方数据
维普
