Huang Dan, Wang Juan, Zeng Yong, Yu Yongxing, Hu Yueming
The School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou 510641, China.
The School of Automotive Science and Engineering, South China University of Technology, Guangzhou 510641, China.
Micromachines (Basel). 2022 Feb 28;13(3):391. doi: 10.3390/mi13030391.
Aiming at the line defect detection of a flexible integrated circuit substrate (FICS) without reference template, there are some problems such as line discontinuity or inaccurate line defect location in the detection results. In order to address these problems, a line feature detection algorithm for extracting an FICS image is proposed. Firstly, FICS image acquisition is carried out by using the appearance defect intelligent detection system independently developed in our lab. Secondly, in the algorithm design of the software system, the binary image of the line image to be segmented is obtained after the color FICS image is classified by K-means, median filtering, morphological filling and closed operation. Finally, for an FICS binary image, an image segmentation model with convexity-preserving indirect regular level set is proposed, which is applied to extract the line features of an FICS image. Experiment results show that, compared with the CV model, LBF model, LCV model, LGIF model, Order-LBF model and RSF model, the proposed model can extract line features with high accuracy, and the line boundary is smooth, which lays an important foundation for high-precision measurement of line width and line distance and high-precision location of defects.
针对无参考模板的柔性集成电路基板（FICS）线路缺陷检测，检测结果存在线路不连续或线路缺陷定位不准确等问题。为解决这些问题，提出一种用于提取FICS图像的线路特征检测算法。首先，利用实验室自主研发的外观缺陷智能检测系统进行FICS图像采集。其次，在软件系统的算法设计中，通过对彩色FICS图像进行K均值分类、中值滤波、形态学填充和闭运算后，得到待分割线路图像的二值图像。最后，针对FICS二值图像，提出一种具有保凸间接正则水平集的图像分割模型，将其应用于提取FICS图像的线路特征。实验结果表明，与CV模型、LBF模型、LCV模型、LGIF模型、Order-LBF模型和RSF模型相比，所提模型能够高精度地提取线路特征，且线路边界光滑，为线路宽度和线路间距的高精度测量以及缺陷的高精度定位奠定了重要基础。
